COMPLEX MULTIDISCIPLINARY SYSTEMS DECOMPOSITION FOR

COMPLEX MULTIDISCIPLINARY SYSTEMS DECOMPOSITION FOR

AEROSPACE VEHICLE CONCEPTUAL DESIGN AND

TECHNOLOGY ACQUISITION

by

AMEN OMORAGBON

Presented to the Faculty of the Graduate School of

The University of Texas at Arlington in Partial Fulfillment

of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY OF AEROSPACE ENGINEERING

THE UNIVERSITY OF TEXAS AT ARLINGTON

August 2016

Committee Chair: Bernd Chudoba

Committee Members: Atilla Dogan

Alan Bowling

Zhen Han

Wen Chan

ii

Copyright © by Amen Omoragbon 2016

All Rights Reserved

iii

ACKNOWLEDGEMENTS

A lot of people have contributed to this doctorate research endeavor academically,

financially and spiritually financially. First and foremost, I give all glory and honor to God

and to His Son Jesus Christ, without whom my life is meaningless. I thank Him for giving

me salvation, health, strength, ability and the resources to accomplish my research

objectives.

Second, I express gratitude to my research mates Amit Oza and Lex Gonzalez.

We embarked together on a near impossible research topic and we persevered together

to achieve success. The hours, sweats and tears invested together are irreplaceable and

I can choose no better people to work with.

Third, I thank my Ph.D. advisor Dr. Bernd Chudoba for professional direction,

compulsion and insight. His role is instrumental because the lessons learned from his

thought processes are invaluable to approach problems. In addition, the access to

Aerospace vehicle design laboratory Data and knowledge bases. they eased the challenge

of literature search. He also provided a network of industry professionals to advise on the

research.

Fourth, I express gratitude to the late Prof. Paul Czysz and my AVD Lab

predecessor Dr. Gary Coleman for instilling in me the multidisciplinary design mindset. I

would also like to thank Eric Haney, Brandon Watters, Reza Mansouri, Doug Coley,

Thomas McCall, James Haley and all of the past and current members of the AVD Lab

with whom I have had of the privilege of working, for their support and friendship throughout

the course of my research.

Fifth, I thank my biological family in Nigeria and my adopted families here in the

United States including the Ibrahims and the Dawodus. Their financial, moral, emotional

and spiritual support made it possible to push through the difficult times. I also thank my

iv

church family at United Christian Fellowship of Arlington and other well-wishers for all the

prayers and words of encouragement they gave throughout the course of this research

endeavor.

Finally, but not the least I thank my soon to be wife Elizabeth for supporting me,

encouraging me, praying with me and believing in me during the most stressful and chaotic

portions of my work.

I pray that the good Lord rewards you all in multiple folds in Jesus name, Amen.

September 9, 2016

v

ABSTRACT

COMPLEX MULTIDISCIPLINARY SYSTEMS DECOMPOSITION FOR

AEROSPACE VEHICLE CONCEPTUAL DESIGN AND

TECHNOLOGY ACQUISITION

Amen Omoragbon, PhD

The University of Texas at Arlington, 2016

Supervising Professor: Bernd Chudoba

Although, the Aerospace and Defense (A&D) industry is a significant contributor to

the United States’ economy, national prestige and national security, it experiences

significant cost and schedule overruns. This problem is related to the differences between

technology acquisition assessments and aerospace vehicle conceptual design. Acquisition

assessments evaluate broad sets of alternatives with mostly qualitative techniques, while

conceptual design tools evaluate narrow set of alternatives with multidisciplinary tools. In

order for these two fields to communicate effectively, a common platform for both concerns

is desired. This research is an original contribution to a three-part solution to this problem.

It discusses the decomposition step of an innovation technology and sizing tool generation

framework. It identifies complex multidisciplinary system definitions as a bridge between

acquisition and conceptual design. It establishes complex multidisciplinary building blocks

that can be used to build synthesis systems as well as technology portfolios. It also

describes a Graphical User Interface Designed to aid in decomposition process. Finally, it

demonstrates an application of the methodology to a relevant acquisition and conceptual

design problem posed by the US Air Force.

vi

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ..........................................................................................................III

ABSTRACT ............................................................................................................................ V

LIST OF ILLUSTRATIONS ......................................................................................................... X

LIST OF TABLES .................................................................................................................. XIII

CHAPTER 1 INTRODUCTION .................................................................................................... 1

1.1 Motivation of Research Topic ............................................................................ 1

1.2 Background of Research topic .......................................................................... 3

1.2.1 Acquisition Lifecycle .................................................................................. 3

1.2.2 Acquisition and Conceptual Design .......................................................... 5

1.2.3 Problems in Conceptual Design Relating to Acquisition ......................... 10

1.2.4 Stakeholder Requirement Definition Solutions ....................................... 11

1.3 Research Scope and Objectives ..................................................................... 13

1.4 Research Approach and Dissertation Outline ................................................. 14

CHAPTER 2 COMPLEX SYSTEMS, AIRCRAFT SYNTHESIS AND PORTFOLIO MANAGEMENT ........ 16

2.1 Complex Systems and Complex Multidisciplinary Systems ............................ 16

2.1.1 Complex Systems ................................................................................... 16

2.1.2 Aerospace Vehicles as Complex Multidisciplinary Systems ................... 20

2.2 Review of Aerospace Vehicle Synthesis Systems .......................................... 23

2.2.1 Aerospace Vehicle Design Synthesis Systems ...................................... 23

2.2.2 Synthesis Systems Compatibility with Acquisition Problem ................... 30

2.2.3 Concepts for Advanced Synthesis Systems ........................................... 38

2.3 Portfolio Planning for Technology Acquisition ................................................. 39

2.3.1 Portfolio Planning Management .............................................................. 40

2.3.2 Synthesis Tool Composition ................................................................... 42

vii

2.4 Chapter Summary and Solution Concept Specification .................................. 43

CHAPTER 3 COMPLEX MULTIDISCIPLINARY SYSTEM DECOMPOSITION CONCEPT .................... 45

3.1 Decomposition into CMDS Blocks ................................................................... 46

3.2 Decomposition into Product Blocks ................................................................. 48

3.2.1 Subsystem Decomposition ..................................................................... 49

3.2.2 Operational Event Decomposition .......................................................... 57

3.2.3 Operational Requirement Decomposition ............................................... 62

3.3 Decomposition into Analysis Process Blocks .................................................. 64

3.3.1 System Analysis Blocks .......................................................................... 65

3.3.2 Disciplinary Analysis Blocks .................................................................... 66

3.4 Decomposition into Disciplinary Method Blocks .............................................. 66

3.5 Chapter Summary............................................................................................ 68

CHAPTER 4 COMPLEX MULTIDISCIPLINARY SYSTEM DECOMPOSITION TOOL .......................... 69

4.1 CMDS Decomposition Methodology................................................................ 70

4.2 CMDS Decomposition Input Forms ................................................................. 71

4.2.1 Product Input form .................................................................................. 71

4.2.2 Analysis Process Input Form .................................................................. 73

4.2.3 Disciplinary Method Input Form .............................................................. 74

4.3 Chapter Summary............................................................................................ 76

CHAPTER 5 COMPLEX MULTIDISCIPLINARY SYSTEM DECOMPOSITION CASE STUDY ............... 78

5.1 Case Study Objectives .................................................................................... 78

5.2 Case Study Problem – AFRL GHV .................................................................. 78

5.3 Case Study Research Strategy ....................................................................... 81

5.4 Part 1: Technology Acquisition Product Portfolio Prioritization ....................... 81

5.4.1 Reference Vehicle Identification from AVD Database ............................ 82

viii

5.4.2 Reference Vehicle Decomposition into Technology Portfolio ................. 84

5.4.3 Candidate Vehicle Composition from Technology Portfolio ................... 87

5.4.4 Candidate Vehicle Portfolio Value Assessment ...................................... 89

5.4.5 Project Vehicle Selection ........................................................................ 90

5.5 Part 2: Conceptual Design Parametric Sizing ................................................. 91

5.5.1 Decomposition of Synthesis Systems at ASE Laboratory ...................... 92

5.5.2 Composition of Sizing CMDS for Candidate Vehicle 10 ......................... 95

5.5.3 Visualization of Candidate Vehicle CV10 Feasibility Solution Space ..... 97

5.6 Case Study Conclusion ................................................................................. 104

5.7 Chapter Summary.......................................................................................... 104

CHAPTER 6 RESEARCH CONCLUSION, CONTRIBUTION AND OUTLOOK ................................. 106

6.1 Conclusion ..................................................................................................... 106

6.2 Contributions and Benefits of CMDS Decomposition .................................... 108

6.3 Future Work ................................................................................................... 108

METHODS LIBRARY SOURCE CODE ................................................................ 110

A.1 Aerodynamics .................................................................................................... 111

AERO_MD0005 ........................................................................................ 111

AERO_MD0006 ........................................................................................ 111

AERO_MD0007 ........................................................................................ 114

A.2 Propulsion .......................................................................................................... 117

PROP_MD0008 ........................................................................................ 117

A.3 Performance Matching ....................................................................................... 118

PM_MD0003 ............................................................................................. 118

PM_MD0008 ............................................................................................. 118

PM_MD0009 ............................................................................................. 118

ix

PM_MD0010 ............................................................................................. 119

A.4 Weight & Balance ............................................................................................... 122

WB_MD0003 ............................................................................................ 122

CV10 CMDS ................................................................................................ 124

B.1 Input File............................................................................................................. 125

B.2 Results ............................................................................................................... 130

REFERENCES .................................................................................................................... 134

BIOGRAPHICAL INFORMATION ............................................................................................ 144

x

LIST OF ILLUSTRATIONS

Figure 1-1 Defense Acquisition Systems Engineering Lifecycle (Redshaw 2009) ............. 5

Figure 1-2 Design Lifecycle Phase (Omoragbon, 2008) ..................................................... 6

Figure 1-3 Review of Aircraft Synthesis Systems ............................................................. 11

Figure 1-4 NASA/DOD Technology Readiness Level Descriptions (NASA) .................... 12

Figure 2-1 Views of complexity (Bar-Yam, 1997) (a) Simple system made complex by

number of disciplines studied (b) Complexity due to multidisciplinary interactions .......... 18

Figure 2-2 System Architecture Specification Concepts (Shashank , 2010) .................... 19

Figure 2-3 Complexity of Aerospace Vehicles Based ....................................................... 21

Figure 2-4 Confluence Diagram ........................................................................................ 22

Figure 2-5: Aerothermodynamics Environment of Hypersonic Vehicles (Hirschel, 2008) 23

Figure 2-6 Evaluation Process of Design Synthesis Systems (Huang 2006) ................... 25

Figure 2-7 Specification Synthesis System AVDS-SAV (Huang 2006) ............................ 26

Figure 2-8 Nassi-Schneiderman diagram for the Loftin design process (Coleman 2010) 27

Figure 2-9 Example Process overview card (Coleman 2010) .......................................... 28

Figure 2-10 Example Methods overview card (Coleman 2010) ....................................... 29

Figure 2-11 Integration and Connectivity .......................................................................... 32

Figure 2-12 Interface Maturity ........................................................................................... 33

Figure 2-13 Influence of New Components or Environment ............................................. 35

Figure 2-14 Prioritization of Technology Development Efforts ......................................... 36

Figure 2-15 Problem Input Characterization ..................................................................... 38

Figure 2-16 Comparison of Program vs Portfolio Approach to R&D (Janiga, 2014) ........ 40

Figure 2-17 Problem Formulation Data Automation Process (Oza 2016) ........................ 41

Figure 2-18 Notional Example of Composability (Petty and Weisel 2003) ....................... 43

Figure 3-1 Technology Innovation and sizing framework ................................................. 46

xi

Figure 3-2 CMDS Decomposition Blocks.......................................................................... 48

Figure 3-3 Product Block Decomposition .......................................................................... 48

Figure 3-4 Example of hierarchal product decomposition ................................................ 49

Figure 3-5 Example of structural decomposition .............................................................. 50

Figure 3-6 Example of functional decomposition .............................................................. 51

Figure 3-7 Functional Subsystem Block Decomposition .................................................. 54

Figure 3-8 Example Shell Vehicle Packages .................................................................... 56

Figure 3-9 X-51A decomposition into hardware implementations .................................... 57

Figure 3-10 Operational Event Decomposition Block ....................................................... 58

Figure 3-11 Example Flight Profile (Jenkinson, 2003) ...................................................... 60

Figure 3-12 Artist rendering of North American XB70 (Bagera, 2008) ............................. 61

Figure 3-13 Operational Requirement Decomposition Blocks .......................................... 64

Figure 3-14 Design Structure Matrix representation of a process (Kusiak 1999) ............. 64

Figure 3-15 Analysis Process Decomposition Blocks ....................................................... 65

Figure 3-16 Disciplinary Method Block Decomposition .................................................... 67

Figure 4-1 AVD DBMS Three Layer Architecture ............................................................. 70

Figure 4-2 Methodology for CMDS Decomposition .......................................................... 71

Figure 4-3 Product Input Form .......................................................................................... 72

Figure 4-4 Analysis Process Input Form ........................................................................... 74

Figure 4-5 Disciplinary Method Input Form ....................................................................... 75

Figure 4-6 Example Methods Library Entry MATLAB m-file (AERO_MD0001.m) ............ 76

Figure 5-1 Generic Hypersonic Vehicle Study Description (Ruttle et al., 2012) ............... 80

Figure 5-2 Technology Acquisition Portfolio Prioritization Roadmap (Chudoba, 2015) ... 82

Figure 5-3 Vehicle Decomposition – Reference Vehicle Listing ....................................... 83

Figure 5-4 Reference Vehicle Decomposition Methodology ............................................. 84

xii

Figure 5-5 Reference Vehicle Decomposition into functional hardware ........................... 85

Figure 5-6 Candidate Vehicle Synthesis Methodology ..................................................... 87

Figure 5-7 Portfolio Value Assessment Methodology ....................................................... 90

Figure 5-8 Composite Candidate Vehicle TRL for risk scenarios ..................................... 90

Figure 5-9 Characteristics of Candidate Vehicle 10 ......................................................... 91

Figure 5-10 Overview of AVDS Synthesis System (Coleman 2010) ................................ 92

Figure 5-11 Design Mapping Matrix of AVDDBMS Product Library ..................................... 93

Figure 5-12 Design Mapping Matrix of AVDDBMS Process and Method Librarires ............ 94

Figure 5-13 Design Structure Matrix for CV10 sizing CMDS blueprint ............................. 97

Figure 5-14 Sample fixed-mission solution space ............................................................ 98

Figure 5-15 Sample fixed mission solution space with carrier weight constraints ............ 99

Figure 5-16 Sample fixed mission solution space with carrier packaging constraints ...... 99

Figure 5-17 Design mission for CV10 solution space ..................................................... 100

Figure 5-18 M6 CV10 carriage weight solution space .................................................... 101

Figure 5-19 M7 CV10 carriage weight solution space .................................................... 101

Figure 5-20 M6 CV10 gross weight landing solution space ............................................ 102

Figure 5-21 M7 CV10 gross weight landing solution space ............................................ 102

Figure 5-22 M6 CV10 carriage size solution space ........................................................ 103

Figure 5-23 M7 CV10 carriage size solution space ........................................................ 103

xiii

LIST OF TABLES

Table 1-1 Aircraft Synthesis Systems (Chudoba, 2001; Huang, 2006; Coleman, 2010) ... 6

Table 2-1 Hierarchy Complexity (Boulding 1977) ............................................................. 19

Table 2-2 Classification of aerospace design synthesis approaches (Chudoba 2001) .... 24

Table 2-3 Selected By-Hand Synthesis Methodologies .................................................... 30

Table 2-4 Selected Computer-Based Synthesis Systems ................................................ 30

Table 2-5 Literature Survey Criteria – System Capability ................................................. 31

Table 2-6 Scope of Applicability to CD Phase .................................................................. 34

Table 2-7 Scope of Applicability to Aerospace Product Types ......................................... 34

Table 2-8 Data Management Survey Criterion ................................................................. 37

Table 2-9 Data Management Capability ........................................................................... 37

Table 3-1 Description of Hardware Function Categories .................................................. 53

Table 3-2 Hardware Attribute Examples ........................................................................... 55

Table 3-3 Description of Mission Types ............................................................................ 59

Table 3-4 Example thrust function modes for a vehicle .................................................... 61

Table 3-5 Mach Number Flow Regimes ........................................................................... 62

Table 3-6 Earth atmospheric layers used as altitude range blocks .................................. 62

Table 4-1 AVDDBMS Layers ................................................................................................ 69

Table 5-1 Lift and Thrust Source Technology Portfolio Attributes .................................... 86

Table 5-2 Technology Portfolio Definition Shell Vehicles ................................................. 88

Table 5-3 Candidate Vehicle Portfolio .............................................................................. 88

Table 5-4 Candidate Vehicle Portfolio (Cont’d) ................................................................ 89

Table 5-5 AVDS Analysis Process Objective Function Block ........................................... 96

Table 5-6 Disciplinary methods selected for composing CV10 Sizing CMDS .................. 96

1

Chapter 1

INTRODUCTION

1.1 Motivation of Research Topic

The Aerospace and Defense (A&D) industry is a significant contributor to the

United States’ economy, national prestige and national security. In 2014, the industry made

$408.5 billion in revenue (Deloitte 2015), part of which was $78.7 billion in aerospace

export-import trade balance which led to the employment of about 700,000 people (DoC

2015). In addition, recent successes of the SpaceX Falcon 9, ULA Atlas and Blue Origin

New Shepard launches have rekindled interests in the Space travel. Furthermore, there

have been significant investments in high-speed technologies, such as Hypersonic Test

Vehicle (HTV), XS-1 and SR-72 to improve U.S. defense systems from global threats.

Although the A&D industry as a whole reported increased profits in 2014, defense

subsector revenues have seen a down turn. The sector saw a $5.4 billion decline in

revenue in 2014 and is expected to reach an all-time low in 2015 (Deloitte 2015). Steinbock

explains that the challenges the US defense faces are cost pressures endangered by

sequestration, limited budgets, bias for short-term defense polices at the expense of

investments in longer term higher risk activities, challenges of defense acquisitions, shift

from defense spin-offs too consumer market spin-ons, hollowing out of the defense

industrial base, erosion of competitive inter-service pressures, lower defense contractor

R&D intensity and rising foreign defense (Steinbock 2014).

The major sources of the revenue decline are cost overruns and schedule delays

experienced by DOD and A&D companies. In “Can We Afford Our Own Future?” Deloitte

predicts that the average program cost overruns may exceed 46 percent by 2019 (Deloitte

2009). The root causes of the problem are identified as:

2

Project management – Activities such as planning, sourcing, assurance, staffing,

finance and integration have increased budget overruns without improving

development cycle time. In addition, managers rely heavily on assumptions about

system requirements, technology, and design maturity, which are consistently too

optimistic.

Politics – Acquisition decisions are biased towards political expediency and not

necessarily performance results. This has resulted in fund shifting to and from

programs in order to hide bad news reports. Thus, undermining well-performing

programs to pay for poorly performing ones.

Supply Chain – Original equipment manufacturers (OEMs) and large platform

contractors are shedding more their manufacturing and subsystem assembly work

and streamlining their supplier base to create greater economies of scale. This has

led to increased supplier dependency and risk of supply bottlenecks.

Technical Complexity – Increasing performance requirements and desire for the

newest technologies that apply the latest theories has led to the design of more

complex vehicles. This has translated to over 500% increase in development

lifecycles since the 60’s.

Talent Shortage – Baby boomers and older workers comprise 70% of the DoD and

civilian AT&L workforce. Coupled with the fact that the US is producing fewer

qualified scientist and engineers and the baby boobers heading for retirement,

causes concern about the talent availability in A&D industry. In addition, A&D

contractors experience shortage of experienced employees with a broad

understanding of systems integration in an industry that is heading toward more

system integration and complexity. This has had a direct effect on cost overruns

and delays.

3

These problems highlight the following motivating problems with technology forecasting:

The need for increase in design efficiency to balance out waste in the development

chain.

The need for increase in design capability to improve correctness and drive

optimism towards realism.

The need to Increase in design transparency to reduce acquisition decision bias.

The need for a methodology that can be adapted for various system integration

environments to allow easier knowledge transfer to incoming engineers.

The need for a platform that allows for communication between different levels of

the development life cycle and supply chain.

1.2 Background of Research topic

1.2.1 Acquisition Lifecycle

The defense acquisition system exists to manage the nation’s investments in

technologies, programs and product support necessary to achieve the National Security

(Brown 2010). It involves the use of systems engineering (SE) processes by government

and industry entities to provide a framework and methodology to plan, manage, and

implement technical activities throughout the acquisition life cycle (DAU 2013, SMC 2010;

USAF 2011; OSD 2015; MSFC 2012). Redshaw (2009) explains that the acquisition

system evolved from system engineering approaches because programs for developing

complex systems exhibit the same features that formalized the systems engineering

process (Redshaw 2009). The evolution of the system engineering process for defense

acquisition is shown in Figure 1-1. The major teams involved in the model are the decision

authority, the development/design & engineering (system integrator) and specialty

engineering (technologist).

4

In the pre-2003 model, the system engineering process only manages the tasks of

the system integrator and does not consider the decision authority and the technologist

(Redshaw 2009). There are two disadvantages of this model. First, the design engineer

was not involved in the definition of requirements and programs were already flawed from

the beginning. Nicolai explains

“ Even when the customer tries very hard to generate a credible set of requirements. Sometimes they are flawed. History is filled with flawed requirements. Some flawed requirements are discovered and changed, some flawed requirements prevail and designs are produced and some flawed requirements are ignored (this one is always risky).”

Second, the verification loop did not place emphasis on the role of test planning, testing

and evaluation of results as major parts of the product development lifecycle (Redshaw,

2009). These flaws speak to the need for collaboration between the decision maker,

synthesis specialist and the technologists. The steps of the 2009 model are

Stakeholder requirements definition – “establishes a firm baseline for system

requirements and constraints…, thus defining project scope”.

Requirements analysis – “examine user’s needs against available technologies,

design considerations, and external interfaces to begin translating operational

requirements into technical specifications”.

Architecture design – develop a “functional architecture to achieve required

capabilities across scenarios from the operational concept; developing a physical

architecture, internal interfaces, and integration plan, synthesizing alternative

combinations of system components; and selecting the optimal design that

satisfies and balances all requirements and constraints”.

Stake holder requirements definition is a shared responsibility between the decision maker

and the system integrator while architecture design allows for the involvement of

5

technologists. In addition, implementation, integration, verification, validation and transition

are explicitly mentioned in the model.

Figure 1-1 Defense Acquisition Systems Engineering Lifecycle (Redshaw 2009)

1.2.2 Acquisition and Conceptual Design

The 2009 acquisition system correlates with the aircraft product development

lifecycle. The aircraft design lifecycle is shown in Figure 1-2. The requirements analysis

phase corresponds to the mission definition where requirements are translated into the

definition of the system. Architecture design corresponds to the three aircraft design

phases, conceptual design (CD), Preliminary Design (PD) and Detail Design (DD). The

implementation step corresponds with flight test, Certification and Manufacturing. The

conceptual design phase determines the feasibility of meeting requirements with a credible

aircraft design (Nicolai, 2010). The CD phase is critical in design because there is most

freedom to change the design without incurring a lot of cost, see Figure 1-2. One of the

key characteristics of the conceptual design phase is synthesis. Synthesis is concerned

with the systematic generation of alternatives in order to create new designs or improve

existing ones (Kusiak, 1995). A wealth of synthesis systems has been developed over the

last 50 years. Table 1 shows an updated comprehensive list of synthesis systems that have

Stakeholder Requirements

Definition

Requirements Analysis

Architecture Design

Requirements

Development

Logical Analysis

Design Solution

Requirements

Analysis

Functional

Analysis &

Allocation

Synthesis

Implementation, Integration, Verification, Validation, Transition

Sp

ecia

lty

En

gin

eerin

g

De

ve

lop

me

nt/

De

sig

n &

En

gin

eerin

g

De

cis

ion

Au

tho

rity

Pre-2003 Policy

Requirements

Generation System

2003 Policy/JCIDS

2004 Baseline DAG

2008 Policy

2009 Interim DAG Required Capabilities

Concept of Operations

Support Concept

Baseline Agreements

Technologies

Design Considerations

Constraints

System Specification

External Interfaces

Functional Baseline

Functional Architecture

Item Specifications

Allocation Baseline

Physical Architecture

Internal Interfaces

Integration Plan

Technical Planning, Decision Analysis, Technical Assessment, Risk Management,

Requirements Management, Configuration/Interface Management

Verification Loop

System Analysis &

Control

Component Design

Software Design

Initial Product Baseline

6

been developed to aid in aircraft design as compiled by (Chudoba, 2001; Huang, 2006;

Coleman, 2010).

Figure 1-2 Design Lifecycle Phase (Omoragbon, 2008)

Table 1-1 Aircraft Synthesis Systems (Chudoba, 2001; Huang, 2006; Coleman, 2010)

Acronym Full Name Developer Primary Application

Years

AAA Advanced Airplane Analysis DARcorporation Aircraft 1991-

ACAD Advanced Computer Aided Design General Dynamics, Fort Worth

Aircraft 1993

ACAS Advanced Counter Air Systems US Army Aviation Systems Command

Air fighter 1987

ACDC Aircraft Configuration Design Code Boeing Defense and Space Group

Helicopter 1988-

ACDS Parametric Preliminary Design System for Aircraft and Spacecraft Configuration

Northwestern Polytechnical University

Aircraft and AeroSpace Vehicle

1991-

ACES Aircraft Configuration Expert System Aeritalia Aircraft 1989-

ACSYNT AirCraft SYNThesis NASA Aircraft 1987-

ADAM (-) McDonnell Douglas Aircraft

ADAS Aircraft Design and Analysis System Delft University of Technology

Aircraft 1988-

ADROIT Aircraft Design by Regulation Of Independent Tasks

Cranfield University Aircraft

ADST Adaptable Design Synthesis Tool General Dynamics/Fort Worth Division

Aircraft 1990

AGARD 1994

AIDA Artificial Intelligence Supported Design of Aircraft

Delft University of Technology

Aircraft 1999

AircraftDesign (-) University of Osaka Prefecture

Aircraft 1990

APFEL (-) IABG Aircraft 1979

Aprog Auslegungs Programm Dornier Luftfahrt Aircraft

ASAP Aircraft Synthesis and Analysis Program

Vought Aeronautics Company

Fighter Aircraft 1974

Conceptual

Design

Preliminary

Design

Detail

Design

Flight Test,

Certification,

Manufacturing

Operations

Incident/

Accident

Investigation

Mission

Definition

Specification

of Desired:

Range

Payload

Flight rate

...

Identification

and Evaluation

of Design

Alternatives

Detailed Analysis

of Promising

Concepts

Development of

Requirements

for Tooling and

Manufacturing

Demonstration of

Airworthiness and

Performance and

Production of Fleet

Life Cycle

Normal

Operation and

Maintenance of

Fleet

CD PD DD FT,C,M O I, AI

Investigation and

recording of

accidents and

interventions

7

ASCENT (-) Lockheed Martin Skunk Works

AeroSpace Vehicle

1993

ASSET Advanced Systems Synthesis and Evaluation Technique

Lockheed California Company

Aircraft Before 1993

Altman Design Methodology for Low Speed High Altitude UAV's

Cranfield University Unmanned Aerial Vehicles

Paper 1998

AVID Aerospace Vehicle Interactive Design N.C. State University, NASA LaRC


1992

AVSYN ? Ryan Teledyne ? 1974

BEAM (-) Boeing ? NA

CAAD Computer-Aided Aircraft Design SkyTech High-Altitude Composite Aircraft

NA

CAAD Computer-Aided Aircraft Design Lockheed-Georgia Company Aircraft 1968

CACTUS (-) Israel Aircraft Industries Aircraft NA

CADE Conceptual Aircraft Design Environment

McDonnel Douglas Corporation

Fighter Aircraft (F-15)

1974

CAP Configuration Analysis Program North American Rockwell (B-1 Division)

Aircraft 1974

CAPDA Computer Aided Preliminary Design of Aircraft

Technical University Berlin Transonic Transport Aircraft

1984-

CAPS Computer Aided Project Studies BAC Military Aircraft Devision

Military Aircraft 1968

CASP Combat Aircraft Synthesis Program Northrop Corporation Combat Aircraft 1980

CASDAT Conceptual Aerospace Systems Design and Analysis Toolkit

Georgia Institute of Technology

Conceptual Aerospace Systems

late 1995

CASTOR Commuter Aircraft Synthesis and Trajectory Optimization Routine

Loughborough University Transonic Transport Aircraft

1986

CDS Configuration Development System Rockwell International Aircraft and AeroSpace Vehicle

1976

CISE (-) Grumman Aerospace Corporation

AeroSpace Vehicle

1994

COMBAT (-) Cranfield University Combat Aircraft

CONSIZ CONfiguration SIZing NASA Langley Research Center

AeroSpace Vehicle

1993

CPDS Computerized Preliminary Design System

The Boeing Company Transonic Transport Aircraft

1972

Crispin Aircraft sizing methodology Loftin Aircraft sizing methodology

1980

DesignSheet (-) Rockwell international Aircraft and AeroSpace Vehicle

1992

DRAPO Définition et Réalisation d'Avions Par Ordinateur

Avions Marcel Dassault/Bréguet Aviation

Aircraft 1968

DSP Decision Support Problem University of Houston Aircraft 1987

EASIE Environment for Application Software Integration and Execution

NASA Langley Research Center


1992

EADS

ESCAPE (-) BAC (Commercial Aircraft Devision)

Aircraft 1995

ESP Engineer's Scratch Pad Lockheed Advanced Development Co.

Aircraft 1992

Expert Executive (-) The Boeing Company ?

FASTER Flexible Aircraft Scaling To Requirements

Florian Schieck

FASTPASS Flexible Analysis for Synthesis, Trajectory, and Performance for Advanced Space Systems

Lockheed Martin Astronautics

AeroSpace Vehicle

1996

FLOPS FLight OPtimization System NASA Langley Research Center

? 1980s-

8

FPDB & AS Future Projects Data Banks & Application Systems

Airbus Industrie Transonic Transport Aircraft

1995

FPDS Future Projects Design System Hawker Siddeley Aviation Ltd

Aircraft 1970

FRICTION Skin friction and form drag code 1990

FVE Flugzeug VorEntwurf Stemme GmbH & Co. KG GA Aircraft 1996

GASP General Aviation Synthesis Program NASA Ames Research Center

GA Aircraft 1978

GPAD Graphics Program For Aircraft Design Lockheed-Georgia Company Aircraft 1975

HACDM Hypersonic Aircraft Conceptual Design Methodology

Turin Polytechnic Hypersonic aircraft

1994

HADO Hypersonic Aircraft Design Optimization Astrox ? 1987-

HASA Hypersonic Aerospace Sizing Analysis NASA Lewis Research Center

AeroSpace Vehicle

1985, 1990

HAVDAC Hypersonic Astrox Vehicle Design and Analysis Code

Astrox 1987-

HCDV Hypersonic Conceptual Vehicle Design NASA Ames Research Center

Hypersonic Vehicles

HESCOMP HElicopter Sizing and Performance COMputer Program

Boeing Vertol Company Helicopter 1973

HiSAIR/Pathfinder High Speed Airframe Integration Research

Lockheed Engineering and Sciences Co.

Supersonic Commercial Transport Aircraft

1992

Holist ? ?

Hypersonic Vehicles with Airbreathing Propulsion

1992

ICAD Interactive Computerized Aircraft Design

USAF-ASD ? 1974

ICADS Interactive Computerized Aircraft Design System

Delft University of Technology

Aircraft 1996

IDAS Integrated Design and Analysis System Rockwell International Corporation

Fighter Aircraft 1986

IDEAS Integrated DEsign Analysis System Grumman Aerospace Corporation

Aircraft 1967

IKADE Intelligent Knowledge Assisted Design Environment

Cranfield University Aircraft 1992

IMAGE Intelligent Multi-Disciplinary Aircraft Generation Environment

Georgia Tech


1998

IPAD Integrated Programs for Aerospace-Vehicle Design


AeroSpace Vehicle

1972-1980

IPPD Integrated Product and Process Design Georgia Tech Aircraft, weapon system

1995

JET-UAV CONCEPTUAL DEISGN CODE

Northwestern Polytechnical University, China

Medium range JET-UAV

2000

LAGRANGE Optimization 1993

LIDRAG Span efficiency 1990

LOVELL 1970-1980

MAVRIS an analysis-based environment Georgia Institue of Technology

2000

MELLER Daimler-Benz Aerospace Airbus

Civil aviation industry

1998

MacAirplane (-) Notre Dame University Aircraft 1987

MIDAS Multi-Disciplinary Integrated Design Analysis & Sizing

DaimlerChrysler Military Aircraft 1996

MIDAS Multi-Disciplinary Integration of Deutsche Airbus Specialists

DaimlerChrysler Aerospace Airbus


1996

MVA Multi-Variate Analysis RAE (BAC) Aircraft 1991

MVO MultiVariate Optimisation RAE Farnborough Aircraft 1973

9

NEURAL NETWORK

FORMULATION Optimization method for Aircrat Design


Aircraft 1998

ODIN Optimal Design INtegration System NASA Langley Research Center

AeroSpace Vehicle

1974

ONERA Preliminary Design of Civil Transport Aircraft

Office National d’Etudes et de Recherches Aérospatiales

Subsonic Transport Aircraft

1989

OPDOT Optimal Preliminary Design Of Transports


Transonic Transport Aircraft

1970-1980

PACELAB knowledge based software solutions PACE Aircraft 2000

Paper Airplane (-) MIT Aircraft

PASS Program for Aircraft Synthesis Studies Stanford University Aircraft 1988

PATHFINDER Lockheed Engineering and Sciences Co.


1992

PIANO Project Interactive ANalysis and Optimisation

Lissys Limited Transonic Transport Aircraft

1980-

POP Parametrisches Optimierungs-Programm

Daimler-Benz Aerospace Airbus


2000

PrADO Preliminary Aircraft Design and Optimisation

Technical University Braunschweig


1986-

PreSST Preliminary SuperSonic Transport Synthesis and Optimisation

DRA UK


PROFET (-) IABG Missile 1979

RAE Artificial Intelligence Supported Design of Aircraft

Royal Aircraft Establishment, Farnborough

Aircraft conceptual design

Early1970’s.

RAM NASA geometric modeling tool

1991

RCD Rapid Conceptual Design Lockheed Martin Skunk Works

AeroSpace Vehicle

RDS (-) Conceptual Research Corporation

Aircraft 1992

RECIPE (-) ? ? 1999

RSM Response Surface Methodology 1998

Rubber Airplane (-) MIT Aircraft 1960s-1970s

Schnieder

Siegers Numerical Synthesis Methodology for Combat Aircraft

Cranfield University combat aircraft Late 1970s

Spreadsheet Program

Spreadsheet Analysis Program Loughborough University Aircraft Design Studies

1995

SENSxx (-) DaimlerChrysler Aerospace Airbus


SIDE System Integrated Design Environment Astrox ? 1987-

SLAM Simulated Langauge for Alternative Modeling

? ?

Slate Architect (-) SDRC (Eds) ?

SSP System Synthesis Program University of Maryland Helicopter

SSSP Space Shuttle Synthesis Program General Dynamics Corporation

AeroSpace Vehicle

SYNAC SYNthesis of AirCraft General Dynamics Aircraft 1967

TASOP Transport Aircraft Synthesis and Optimisation Program

BAe (Commercial Aircraft) LTD


10

TIES Technology Identification, Evaluation, and Selection


1998

TRANSYN TRANsport SYNthesis NASA Ames Research Center


1963- (25years)

TRANSYS TRANsportation SYStem DLR (Aerospace Research) AeroSpace Vehicle

1986-

TsAGI Dialog System for Preliminary Design TsAGI Transonic Transport Aircraft

1975

VASCOMPII V/STOL Aircraft Sizing and Performance Computer Program

Boeing Vertol CO. V/STOL aircraft 1980

VDEP Vehicle Design Evaluation Program NASA Langley Research Center


VDI

Vehicles (-) Aerospace Corporation Space Systems 1988

VizCraft (-) Virginia Tech


1999

Voit-Nitschmann

WIPAR Waverider Interactive Parameter Adjustment Routine

DLR Braunschweig AeroSpace Vehicle (Waverider)

X-Pert (-) Delft University of Technology

Aircraft Paper 1992

1.2.3 Problems in Conceptual Design Relating to Acquisition

The introduction of stakeholder requirements definition as a responsibility for the

conceptual designer presents new challenges for the current aerospace synthesis

systems. First, typical conceptual design tools and methodologies are not designed to

provide the information required for requirements definition. Figure 1-3 shows a review of

selected methodologies. They each have elements for designing, building and integration

architectures for analysis; however, none of them prescribe a methodology for stakeholder

requirements definition. Second, requirements definition typically requires analysis of a

broad range of alternatives (DAU 2013). However, most synthesis systems have a narrow

range of alternatives that can be analyzed. This is because most of those decisions have

already been made before the synthesis step. Finally, conceptual design methodologies

need to be rapid turn-around at giving solutions to the decision makers in order to avoid

incorrect assumptions and decision-making during the early project phase.

11

Figure 1-3 Review of Aircraft Synthesis Systems

1.2.4 Stakeholder Requirement Definition Solutions

Methodologies exist to provide the capability to evaluate technologies for defense

acquisition. Azizan does a comprehensive review of the assessment approaches available

and categorizes them into qualitative, quantitative and automated techniques. The

qualitative techniques involve use of perceived maturity levels of technology the most

common of which is Technology Readiness Level (Azzizan, 2009; Cornford, 2004; Nolte,

2004; Bilbro, 2009; Dubos, 2007; Mankins, 2007; Mankins, 2002; Ramirez-Marquez, 2009;

Smith, 2009). TRL uses a 9 level scale to present the state of technology as scene in Figure

1-4. The biggest drawback of the TRL measure is that it accounts only for the maturity of

individual technologies, however it doesn’t capture the complexity of packaging those

technologies together as would be for aerospace vehicles. Other Maturity scales have been

created to capture more information than TRL and they include Manufacturing readiness

level (Cundiff, 2003), Integration readiness level (Gove, 2007), TRL for non-system

technologies (Graettinger, 2002), TRL for Software (DOD, 2005), Technology Transfer

Stakeholder Requirements Definition


Design

Build

Integrate

Execute

Stakeholder Requirements Definition


Design

Build

Integrate

Execute

De

cisi

on

Mak

er

Syn

the

sis

Spe

cial

ist

De

cisi

on

Mak

er

Syn

the

sis

Spe

cial

ist

Wood Corning Nicolai Loftin Torenbeek Stinton Roskam

AAA PrADO VDK ACES ACSYNT ASAP FLOPS

Raymer

AVDS

Jenkinson Howe Shaufele

ASTOS V8 HAVDACROSETTA

BY-HAND AEROSPACE SYNTHESIS SYSTEMS

COMPUTER BASED AEROSPACE SYNTHESIS SYSTEMS

Static Input Methodological Input TBD

12

Level Readiness Level (Holt, 2007), Missile Defense Agency checklist (Mahafza, 2005),

Moorhouses Risk Versus TRL Metric (Moorehouse, 2002), Advancement Degree of

Difficulty (AD2) (Bilbro, 2007) and Research and Development Degree of Difficulty (RD3).

Qualitative techniques have the advantages of being quick and easily updatable; however,

they are based on subjective knowledge and do not have a means to consider uncertainties

in the knowledge.

Figure 1-4 NASA/DOD Technology Readiness Level Descriptions (NASA)

Quantitative techniques are prescribed mathematical models for translating

qualitative metrics into numerical data that gives more insight into the maturity of the

technologies. for example, System Readiness Level developed by Sauser uses matrix

manipulations to combine individual subsystem TRLs and IRLs based on the interactions

with one another to describe the maturity of the subsystem technologies as a result of them

combining them into a single system (Saucer 2006, 2007, 2008). Other quantitative

13

techniques include SRL Max (Ramirez-Marquez 2009) Technology Readiness and Risk

Assessment (TRRA) (Mankins 2007), Integrated Technology Analysis Methodology (ITAM)

(Mankins 2002), TRL for Non Developmental Item (NDI) Software, Technology insertion

(TI) Metric (Dowling and Pardo 2005) and TRL Schedule Risk Curve (Dubos et al 2007).

The quantitative techniques are very useful in giving a decision maker analytic data for fact

based decision making; however, they can be difficult to understand and cause information

overload if used improperly.

Automated techniques use spreadsheets or calculators to evaluate the maturity of

technologies. They reduce subjective bias by converting the evaluation into smaller

questions and surveys that are converted into analysis data. They include TRL calculator

(Nolte 2004), MRL Calculator, Technology Program Management Model (TPMM) (SMDTC

2006) and UK MoD System Readiness Level.

The biggest drawback of these acquisition tools is that they do not prescribe a

means of including vehicle mission performance information. Vehicle performance

information is generally a result of sizing and synthesis. Secondly, they do not account for

the supply chain problem and do not include metrics determined from business process

analyses.

1.3 Research Scope and Objectives

The breath of the problems in the Aerospace & Defense industry are broad,

covering acquisition lifecycle simulation, conceptual design and business processes. This

writing, will not attempt to solve all these problems; instead this discussion will answer the

following research questions:

[RQ1] What data relationships are required to connect existing conceptual design

synthesis with acquisition assessment?

14

[RQ2] What are the building blocks required to make conceptual design tools

adaptable to solve emerging aerospace problems in the new acquisition

assessment environment?

[RQ3] How can the methodology that bridges the gap between acquisition and design

decision making be used?

1.4 Research Approach and Dissertation Outline

The framework for solving the acquisition problem was too large to be solved by a

single PhD; therefore, a research endeavor has been taken in conjunction with two other

PhD candidates: Lex Gonzalez and Amit Oza. The unique contribution to this effort by

Amen Omoragbon has been to define the building blocks for the solution architecture, while

Gonzalez (2016) has been tasked to design the software interfaces for the composable

architecture to tailoring tools to problems, and Oza (2016) prescribed the proper utilization

of the system to solve relevant acquisition problems.

In this research thesis, Chapter 1 discusses the motivation of the research which

is the need to improve technology acquisition decision-making from an aerospace

conceptual designer view point. Chapter 2 explores available aerospace synthesis

literature evaluating them in terms of technology adaptability, analysis capability and

data/knowledge management. The result being a specification for a decomposition

methodology for an aerospace decision support system. Chapter 3 describes the Complex

Multidisciplinary System (CMDS) decomposition concept for bridging the gap between

aerospace technology acquisition and aerospace vehicle conceptual design. This

decomposition concept is the original contribution to aerospace science and engineering,

in particular the engineering decision support system developed in collaboration with

Gonzalez and Oza in the ASE Laboratory. Chapter 4 discusses the software

implementation of the CMDS decomposition concept. Chapter 5 discusses the application

15

of the CMDS decomposition concept to a relevant acquisition and conceptual design

problem posed by United State Air Force Research Laboratory. Finally, Chapter 6

summarizes the original contribution of this research effort to aerospace science and gives

an outlook for future work.

16

Chapter 2

COMPLEX SYSTEMS, AIRCRAFT SYNTHESIS AND PORTFOLIO MANAGEMENT

Chapter 1 discussed the need for establishing the data relationships between

aircraft conceptual design and acquisition lifecycle assessment. These data relationships

are key in decreasing cost and schedule overruns in the acquisition lifecycle. This chapter

sets the framework of the solution concept by representing aerospace vehicles as complex

systems that require multidisciplinary synthesis for their design. The Innovation Portfolio is

then introduced as the link between acquisition and conceptual design. This information

will be used to construct the solution concept methodology in Chapter 3. The review

includes a survey of complex multidisciplinary systems, synthesis tools and innovation

portfolios.

2.1 Complex Systems and Complex Multidisciplinary Systems

The term ‘system’ has a broad meaning. Kline (1995) gives three definitions of a

‘system’. First, a system is the object of study, what we want to discuss, define, think about,

write about, and so forth. This means that a system is anything that we care about.

Secondly, a system is a picture, equation, mental image, conceptual model, word

description, etc., which represents the entity we want to discuss, analyze, think about, write

about. This implies that a representation of a system is a system in itself. Thirdly, a system

is an integrated entity of heterogeneous parts which acts in a coordinated way. This

definition gives the idea that a system comprises unique parts that perform actions. In

addition, it allows the introduction of the concept of system complexity.

2.1.1 Complex Systems

A complex system is defined as one that requires a lot of information in order to

describe it. Bar-Yam (1997) characterizes complexity of system elements, their number,

17

the interactions, their strength, formation/operation and their time scales,

diversity/variability, environment and its demands, activities and their objectives. Table 2-1

Boulding (1956) gives a hierarchy of system complexity based on the prevailing scientific

understanding. This shows that system complexity changes with types of disciplines

considered in the representation of the system. It also speaks to the multidisciplinary nature

of complex systems. A simple system can be made complex if it is to be studied by taking

multiple disciplinary points of view into account as shown in Figure 2-1. Complex systems

retain definitions across philosophy, theory and application as show in Table 2-1. Shashank

(2010) summarizes the measures of complexity as:

Level of Abstraction: Complexity measured through “… the visualization of system

at different levels of detail …” such as system level (e.g. vehicle as a whole),

subsystem level (e.g. wing), component level (e.g. wing spars) as shown in Figure

2-2.

Type of representation – Complexity resulting from how the system is modeled at

each level of abstraction.

Size – Complexity based on the number of components and interactions within the

system.

Heterogeneity – Complexity based on the number of unique components and

interactions. This is similar to the size measure. However, it takes into account the

fact that the system with repeating components and interactions can be simplified.

Coupling – Complexity based on the types of interactions between the

components. There can be direct coupling, where the components are physically

connected, or indirect coupling, where one component affects another without a

physical link.

18

Modularity – Complexity due to a group of components coupled together in order

to provide a function.

Uncertainty – Complexity due to the potential of a system to exhibit unexpected

behavior.

Dynamics – Complexity due to the variation of system behavior over time.

Off-Design interactions – Complexity due to the system operating outside its

design range.

Figure 2-1 Views of complexity (Bar-Yam, 1997) (a) Simple system made complex by

number of disciplines studied (b) Complexity due to multidisciplinary interactions

19

Table 2-1 Hierarchy Complexity (Boulding 1977)

Level Characteristics Examples Relevant Discipline

1. Structure Static Crystals Any

2. Clock-works Pre-Determined motion

Machines, the solar system

Physics, Chemistry

3. Control Mechanism

Closed-loop control Thermostats, mechanisms in organisms

Cybernetics, Control Theory

4. Open Systems

Structurally Self maintaining

Flames, biological cells

Information Theory, Biology (metabolism)

5. Lower Organisms

Organized whole functional parts, growth, reproduction

Plants Botany

6. Animals A brain to guide total behavior ability to learn

Birds and Beasts Zoology

7. Humans Self-consciousness, knowledge symbolic language

Humans Psychology, Human Biology

8. Socio-cultural systems

Roles communication, transmission of values

Families, clubs, organizations, nations

Sociology, Anthropology

9. Transcendental systems

Inescapable unknowables

God Metaphysics, Theology

Figure 2-2 System Architecture Specification Concepts (Shashank , 2010)

20

Ryan has shown that Complex Systems can be used to answer a broad range of

problems if a multidisciplinary approach is used to their design. He remarks that “… of all

the systems approaches, complex systems are the most tightly integrated with the natural

sciences, due to an emphasis on explaining mechanism in natural systems …” Therefore,

the representation of objects of study as complex systems gives the most suitable starting

point for analysis. In the context of this research, the term complex multidisciplinary system

(CMDS) is used. This is because the goal is to build a methodology for assessing the risk

and value of emerging aerospace technology from an acquisition and conceptual design

point of view. These systems are complex because of their limited understanding and

numerous highly integrated parts. The word ‘multidisciplinary’ has been added to Complex

Systems in order to emphasize that they need to be studied from more than a single-

discipline perspective.

2.1.2 Aerospace Vehicles as Complex Multidisciplinary Systems

Aerospace Vehicles are can be represented as complex systems. They have

multiple levels of abstraction, various types of representation, a large number of unique

parts and interactions and experience changing dynamic behavior as they operate within

and outside their design conditions. There are numerous classification scales for

aerospace vehicles. These scales include the mission objectives scale, the investment

sector scale, the reusability scale, the staging concept scale, the trajectory segment scale,

and the aerothermodynamics scale among others. Figure 2-3 shows a spectrum of these

cascading scales and sub-levels, and further options for consideration vehicle acquisition

during the conceptual design of aerospace vehicles. As shown on the left side of the figure,

the possible permutations of acquisition and design options rise exponentially. This multi-

disciplinary phenomenon requires management of the inherent complexity accordingly.

21

Figure 2-3 Complexity of Aerospace Vehicles Based

Complexity also tends to increase as the vehicle design speed increases. As a

consequence, high speed vehicles are placing highest demands on the vehicle

technologies and their respective interdisciplinary couplings. This results in vehicles that

are more integrated. Figure 2-4 shows the confluence of vehicle geometry and

technologies as the design cruise Mach number increases. High speed missions also

introduce new disciplinary considerations, such as aerothermodynamics which are not

major concerns at slow speeds. Hirschel (2008) classifies hypersonic vehicles based on

the aerothermodynamics environment they experience throughout their design missions

as shown in Figure 2-5. The vehicles classes are Reentry Vehicle (RV), Cruise and

Acceleration Vehicle (CAV), Ascent and Reentry Vehicle (ARV) and Aeroassisted Orbital

Transfer Vehicle (AOTV). RVs are vehicles which do not cruise or accelerate in a

hypersonic environment; however, they decelerate from very high velocities. CAVs cruise

`

Total

Options

Level

Options

4

12

24

48

144

4

3

2

2

3

Mis

sio

n F

ore

ca

sti

ng

Mission Options

331,7763

1,990,6566

Reusability

Reusable Expendable

UnmannedManned

Human Rating

Staging Concept

MultiTwoSingle

Forecast Components

Data-Base

Knowledge-Base

Parametric Process

Aerodynamics

Propulsion

Structures

Configuration

Performance

Etc...

TechnologyEnvironmentPoliticalSocialEconomicMarket

Environmental Forces

Mission & System Implementation Spectrum

Point-to-Point EscapeOrbitalSub-Orbital

Mission Objective

ResearchCommercialMilitary

Investment Sector

Trajectory Segment

Orbit

Mis

sio

n D

efi

nit

ion

De

cis

ion

Se

qu

en

ce

110,592768

Take-Off

Air-LaunchVerticalHorizontal

Ascent / Climb

Lifting Non-Lifting

Cruise

V=0 ALT=0

Orbital Transfer

Propulsive Aerodynamic

Descent / Reentry

Lifting Non-Lifting

Approach / Landing

BallisticVerticalHorizontal Air-SnatchParabolicEllipticCircular Hyperbolic

22

or accelerate in a hypersonic environment; however, they do not reach very high

hypersonic velocities. ARVs accelerate to and decelerate from high hypersonic velocities.

AOTVs are in-space vehicles which briefly enter the atmosphere to change orbit. Each

vehicle class is configured and optimized to maximize performance in their hypersonic

environments. Their design missions have to be well optimized. Most noticeably, lifting or

non-lifting flight paths are selected in order to create high or low drag conditions dependent

on desired cross-range and down-range requirements. In summary, the complexities of

aerospace vehicles need to be managed early in the design process and this burden clearly

falls on the designer and the synthesis specialists modeling the total system.

Figure 2-4 Confluence Diagram

23

Figure 2-5: Aerothermodynamics Environment of Hypersonic Vehicles (Hirschel, 2008)

2.2 Review of Aerospace Vehicle Synthesis Systems

2.2.1 Aerospace Vehicle Design Synthesis Systems

Design synthesis involves the generation of one of more design solutions

consistent with the requirements defined during the formulation of the design problem and

any additional requirements identified during synthesis (Krishnamoorthy, 2000). Chudoba

(2001), Huang (2006), Colman (2010) and Gonzalez (2016) review the state of the art in

aerospace synthesis systems. Chudoba (2001), provides an assessment of aircraft

synthesis systems, detailing specifically the change in modeling complexity as a function

of time. He explains, “… The classification scheme selected distinguishes the multitude of

vehicle analysis and synthesis approaches according to their modeling complexity, thereby

expressing their limitations and potential. …” Table 2-2 shows the characteristics of the five

different classes of flight vehicle synthesis. The classes measure the chronological

24

implementation and integration of design knowledge with computer automation in

aerospace design. Chudoba postulates that Class V synthesis capability with emphasis on

the integration of multi-disciplinary effects, and the use of dedicated methods libraries as

a necessity to keep up with ever changing acquisition demands and emerging technology

advancements. The characteristics of this generation of synthesis systems include Generic

& Physical Methods, Life-Cycle Synthesis, Knowledgebase System, Multidisciplinary

Optimization, Multi-Fidelity, Design Skill, Methods Library, Integrated People Management

Process.

Table 2-2 Classification of aerospace design synthesis approaches (Chudoba 2001)

The result of this review and subsequent classification scheme has been the

specification of the ‘Class V – Generic Synthesis Capability’. This breakdown places

emphasis on the integration of multi-disciplinary effects, and the use of dedicated methods

libraries. It is important to note that Chudoba defines Class V Synthesis as a design

process NOT a design tool. This implies that more emphasis should be placed on

developing the capability of a synthesis system and holistic perspective for involving the

design team as opposed to the implementation of the tool itself. Chudoba specifies the

attributes of a Class V system as follows: Generic & Physical Methods, Life-Cycle

Synthesis, Knowledgebase System, Multidisciplinary Optimization, Multi-Fidelity, Design

Skill, Methods Library, Integrated People Management Process.

25

Huang (2006) assesses 115 aerospace synthesis systems meant for the design of

aircraft, helicopters, missiles and launch vehicles. He evaluates a cross-section of the year

2004 state-of-the-art synthesis systems through a systematic evaluation process, providing

an overview of each system with detail about its applicability towards the Space Access

Problem as shown in Figure 2-6. Huang categorized each system according to its ability to

perform the following: Mathematical Modelling, Multidisciplinary Analysis and Optimization,

Knowledge-Based System, and Generic Concepts. The result showed a discrepancy in the

ability of the then state of the art, circa 2004, to adequately address the Space Access

Vehicle (SAV) problem in the early stages of conceptual design. This led him to the

following specifications for a synthesis system for SAV, see Figure 2-7. Of note in Figure

2-7 is the inclusion of a ‘Database Management System’. This addition to the ‘Class V

Synthesis’ specification reveals the necessity of the system to not only connect design

parametric data but to also to ‘control utilization of the design methods library’.

Figure 2-6 Evaluation Process of Design Synthesis Systems (Huang 2006)

26

.

Figure 2-7 Specification Synthesis System AVDS-SAV (Huang 2006)

Coleman (2010) investigates synthesis systems applicable to the early conceptual

design for both, conventional and novel vehicle configurations. Coleman shows that,

although parametric sizing is the most critical step during the conceptual design phase, it

“… has stagnated or has been ignored in the current literature …” He then introduces a

specification advancing the state of the art in parametric sizing of aerospace vehicles: (1)

Development of a conceptual design process library, (2) Development of a conceptual

design parametric sizing methods library, (3) Development of an integrated and flexible

parametric sizing program based on the process and methods library. For Coleman,

27

separation of the analysis processes from disciplinary methods is instrumental in his

development of the Aerospace Vehicle Design System, a state-of-the-art generic aircraft

parametric sizing methodology and tool.

The analytic process describes the major steps taken by the synthesis system.

The process library assembled by Coleman documents the processes implemented in

existing synthesis systems using Nassi-Schniederman (NS) process diagrams and

process cards. The NS diagrams visualize input, analysis, output and iteration steps for

the process using color coding to show the applicability of the steps to parametric sizing,

see Figure 2-8. The process card provides a written overview, application, and

iinterpretation of the process as shown in Figure 2-9. The overview section contains

indexing information including authors, publication date (both current and initial), and

published references. The application of the process section provides context towards

when and where the implementation should be used. The last section, interpretation’,

discusses how well the process answers the problem it was intended to solve.

Figure 2-8 Nassi-Schneiderman diagram for the Loftin design process (Coleman 2010)

Loftin Design Process

Calculate performance constraints: W/S and T/W

Mission requirements, design trades, mission profile

Take-off Field Length: T/W=f(W/S)

Landing field length and aborted landing: W/S

2nd Segment climb gradient: T/W

Cruise: T/W=f(W/S)

Construct performance matching diagram: based on performance constrains. Select match point, T/W and W/S

Compute Wto, Wf/Wto,

Compute T, S, and fuselage size

Construct performance map

Initial concept research

Define geometry trade studies, AR, LLE, Propulsion system

Climb performance: T/W=f(W/S)

Parametric sizing

Conceptual design evaluation

Configuration component design

Key

28

Figure 2-9 Example Process overview card (Coleman 2010)

Methods describe the application of disciplinary principles or empirical data to

determine effects in the analysis. The ‘Methods Library’ consists of disciplinary methods

accumulated intoa compendium as either parts of a synthesis system, or as standalone

analytic methods library. Each entry in the library is represented in a card detailing

Processes Overview

Design Phases

Conceptual Design

Author

Loftin

Initial Publication Date

1980

Latest Publication Date

1980

Reference: Loftin, L., “Subsonic Aircraft: Evolution and the Matching of Sizing to Performance,” NASA RP1060, 1980

Application of Processes

Applicability

Primarily focused on parametric sizing of jet powered transports and piston powered general aviation aircraft

Objective of Processes

Determine an approximate size and weight the aircraft to complete the mission from a 1st level

approximation of the design solution space

Initial Start Point

The processes begins with mission specification, possible configurations and fixed design variables such as AR.

Description of basic execution

From the mission specification statistics and basic performance relationships are used to determine relationships between T/W and W/S (Performance matching). The aircraft is then sized around this match point

Interpretation

CD steps

Parametric Sizing

Synthesis Ladder

Analysis

Integrate

Iteration of design

Visualize design space

Similar Procedures

Roskam (preliminary sizing)

Torenbeek (Cat 1 methods)

General Comments:

One of the first published processes utilizing performance matching

Where Nicolai compares T/W and W/S after the complete convergence and interaction of the processes, Loftin derives basic relationships between T/W up front to visualize the solution space before intial sizing.

Loftin essential short cuts the Nicolai approach to derive an initial design space rather than an initial configuration.

29

assumptions, applicability, basic procedure, and experience. The accumulated disciplinary

methods library allows for the documentation and storage of design experience/knowledge

in a centralized location. This results in the ability of the designer to choose which method

is best suited for the given problem.

Figure 2-10 Example Methods overview card (Coleman 2010)

Method Overview

Discipline

Aerodynamics

Design Phase

Parametric Sizing

Method Title

Initial Drag polar estimation

Categorization

Semi-Empirical

Author

Roskam

Reference: Roskam, J., “Airplane Design Part I: Preliminary Sizing of Airplanes,” DARcorporation, Lawrence, Kansas, 2003

Brief Description

The drag polar is constructed using empirical relationships for parasite drag (based on gross weight), flap and landing gear effects. A classical definition of induced drag is used.

Assumptions

Increments of flap and landing gear taken from typical values

Parasite drag coefficient is a function of take-off gross weight

Applicability

Homebuilt aircraft propeller aircraft, single engine propeller aircraft, twin engine propeller aircraft, agricultural aircraft, business jets, regional turboprop aircraft, transport jets, military trainers, fighters, military patrol, bomb and transport, flying boats, supersonic cruise aircraft

Execution of Method

Input

Mission profile, type of aircraft, take-off gross weight, AR, e, S estimate

Analysis description

Estimate Swet=f(WTO) empirical based on type of aircraft Fig 3.22

Estimate f=f(Swet) empirical based on type of aircraft Fig 3.21

Assume average value of S

Select Flap and landing gear effects for each mission segment Table 3.6

eAR

CCCSfC L

DLGDflapD

2

/

Assume CLmax values from Table 3.1

Output:

Drag Polar

Experience

Accuracy

Unknown

Time to Calculate

Unknown

General Comments

30

2.2.2 Synthesis Systems Compatibility with Acquisition Problem

In order to understand the applicability of existing synthesis systems to the

acquisition problem, a review has been conducted in conjunction with Gonzalez (2016) and

Oza (2016). The synthesis systems chosen for the review are representative ‘By-Hand

Synthesis Methodologies’ as shown in Table 2-3, and Computer-Based Methodologies’ as

shown in Table 2-4. The by-hand methodologies or handbook methods originate from

design text books, short courses to company internal methods, while the computer-based

methodologies take advantage of digital processing power.

Table 2-3 Selected By-Hand Synthesis Methodologies

Author Year Title

Corning 1979 Supersonic and Subsonic, CTOL and VTOL, Airplane Design()

Howe 2000 Aircraft Conceptual Design Synthesis()

Jenkinson 1999 Civil Aircraft Design()

Loftin 1980 Subsonic Aircraft: Evolution and the Matching of Size to Performance()

Nicolai 2010 Fundamentals of aircraft and airship design Volume 1, Aircraft design()

Raymer 1999 Aircraft Design: A Conceptual Approach()

Roskam 2004 Airplane Design, Parts I-VIII()

Schaufele 2000 The Elements of Aircraft Preliminary Design()

Stinton 1998 The Anatomy of the Airplane()

Torenbeek 1982 Synthesis of Subsonic Airplane Design()

Wood 1963 Aerospace Vehicle Design Vol. 1, Aircraft Design()

Table 2-4 Selected Computer-Based Synthesis Systems

Acronym Year Full name Developer

AAA 1991- Advanced Airplane Analysis() DARcorporation

ACSYNT 1987- AirCraft SYNThesis() NASA

AVDS 2010 Aerospace Vehicle Design System() Aerospace Vehicle Design Laboratory

CADE 1968 Computer Aided Design Evaluation McDonnell Douglas

FLOPS 1994- FLight OPtimization System() NASA Langley Research Center

Model Center 1995- Model Center Integrate - Explore - Organize() Phoenix Integration Inc

pyOPT 2012- Python-based object-oriented framework for nonlinear constrained optimization()

Royal Military College of Canada

PrADO 1986- Preliminary Aircraft Design and Optimisation() Technical University Braunschweig

31

VDK/HC 2001 VDK/Hypersonic Convergence() McDonnell Douglas, Hypertec

The synthesis methodology capability review assesses the ability of synthesis

systems to characterize, analyze, and solve classical and new/novel aerospace problems.

The assessment criteria are shown in Table 2-5. (1) Integration and connectivity is related

to ability of the system to model vehicles at the subsystem level of abstraction with

multidisciplinary considerations. (2) Interface maturity studies the ability of the synthesis

system to combine hardware pieces together and analyze multidisciplinary effects resulting

from interfacing them. (3) The scope displays the conceptual design activity and the type

of product (aerospace vehicle) to which each synthesis is applicable. (4) Influence of New

Components or Environments explores the adaptability of the synthesis systems to

emerging technologies and requirements. (5) Prioritization of Technology development

efforts shows the flexibility of the system to match changing fidelity and data requirements

during the product lifecycle. (6) Methodological problem requirement indicates if the

synthesis system provides a methodology for the problem of stakeholder requirements and

requirements analysis. The results of the survey are shown in Figure 2-11 to Figure 2-15

and Table 2-6 to Table 2-9.

Table 2-5 Literature Survey Criteria – System Capability

a Can assess each hardware technology independently

b Can assess multiple disciplinary effects for each hardware

a Can combine hardware technologies to form a vehicle

b Can combine hardware technology disciplinary effects

a Conceptual design phase applicability

b Product applicability

a Modular hardware technologies

b Modular mission types

c Modular disciplinary analysis methods

a Able to match hardware technology disciplinary models to problem requirements

b Data management capability

a Methodological problem requirements

System Capability

3. Scope of Applicability

2. Interface Maturity

1. Integration & Connectivity

4. Influence of New Components or Environment

5. Prioritization of Technology Development Efforts

6. Problem Input Characterization

32

Figure 2-11 Integration and Connectivity

Figure 2-11 shows that the by-hand methods all have the capability to analyze

individual hardware components outside of the synthesis process loop. This is because of

the freedom to use various texts/sources as the designers see fit. On the other hand,

computer systems have more integrated methodologies with prescribed order of

operations that must be followed. AVDS and VDK/HC (Czysz and Vandenkerckhove, 2001)

especially are not set up to run individual hardware performance outside of the main design

loop. Model Center (Davies, 2015)is an open platform which initially does not contain any

vehicle-specific methodology, the user has to develop a custom design framework by

integratinge user specified methods.

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Ye

sN

oY

es

No

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Ye

sN

oY

es

No

1.b - Can assess multiple disciplinary effects for each hardware

Computer BasedBy-Hand

Computer Based

1.a - Can assess hardware technology independently

By-Hand

33

Figure 2-12 Interface Maturity

Figure 2-12 shows that all the by-hand and computer-based systems have the

capability to use buildup methods towards vehicle hardware. Each system represents the

vehicle as a composition of hardware pieces. All of the systems surveyed are able to

combine the effects of hardware pieces to solve for the total vehicle effect. Loftin and Wood

are unable to represent vehicle disciplinary effects as a composition of individual hardware

effects because both methodologies solely use empirical methods for disciplinary analysis.

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Ye

sN

oY

es

No

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Ye

sN

oY

es

No

2.b - Can combine individual hardware technology disciplinary effects


Computer Based

2.a - Can combine hardware technologies to form a vehicle

By-Hand

34

Table 2-6 Scope of Applicability to CD Phase

Table 2-7 Scope of Applicability to Aerospace Product Types

Corning

1979

Howe

2000

Jenkinson

1999

Loftin

1980

Nicolai

2010

Raymer

2006

Roskam

1985

Schaufele

2000

Stinton

1998

Torenbeek

1982

Wood

1963

AAA

1991

ACSYNT

1987

ASAP

1974

AVDS

2010

FLOPS

1980

Model

Center

PrADO

1986

pyOPT

2012

VDK/HC

2000

Parametric Sizing Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Yes

Configuration Layout Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No No No

Configuration Evaluation Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes No No Yes No Yes Yes No

N/A No No No No No No No No No No No No No No No No Yes No No No

By-Hand Computer

Corning

1979

Howe

2000

Jenkinson

1999

Loftin

1980

Nicolai

2010

Raymer

2006

Roskam

1985

Schaufele

2000

Stinton

1998

Torenbeek

1982

Wood

1963

AAA

1991

ACSYNT

1987

ASAP

1974

AVDS

2010

FLOPS

1980

Model

Center

PrADO

1986

pyOPT

2012

VDK/HC

2000

HomebuiltNo No No Yes No Yes Yes No Yes No Yes Yes No No No Yes No Yes Yes No

Single EngineNo No No Yes No Yes Yes No Yes No Yes Yes No No No Yes No Yes Yes No

Twin EngineNo No No Yes No Yes Yes No Yes No Yes Yes No No No Yes No Yes Yes No

AgriculturalNo No No Yes No Yes Yes No Yes No Yes Yes No No No Yes No Yes Yes No

Business JetYes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes No Yes Yes No

Regional TBP'sYes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes No Yes Yes No

Transport AircraftYes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes No Yes Yes No

Mil. TrainersNo No No No No No No No No No No No No No No No No No No No

FightersNo No No No Yes Yes Yes No No No No Yes Yes Yes No Yes No No Yes No

Mil. Patrol, bombers,

transportNo No No No No No No No No No No No No No No No No No No No

Flying boats, AmphibiousNo No No No No Yes Yes No No No No Yes No No No No No No Yes No

Supersonic CruiseYes No No No Yes Yes Yes No No No Yes Yes Yes Yes Yes Yes No No Yes No

Hypersonic P2P No No No No No No No No No No No No No No Yes No No Yes No No

Launcher (Rocket)No No No No No No No No No No No No No No No No No No No Yes

Launcher (A/B)No No No No No No No No No No No No No No No No No No No Yes

ReentryNo No No No No No No No No No No No No No Yes No No No No No

In-SpaceNo No No No No No No No No No No No No No Yes No No No No No

N/ANo No No No No No No No No No No No No No No No Yes No No No

By-Hand Computer

35

Table 2-6 shows that all systems have methodologies for the parametric sizing

step of the conceptual design phase, except Model Center which is a blank canvas and

PrADO which is designed for the later conceptual design steps. Table 2-7 shows the

applicability to different aerospace vehicle missions. Most systems are suitable for

commercial transports, only VDK is applicable to launchers and no system covers the

entire mission spectrum.

Figure 2-13 Influence of New Components or Environment

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Ye

sN

oY

es

No

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Ye

sN

oY

es

No

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Ye

sN

oY

es

No

Computer Based

4.a - Modular hardware technologies

By-Hand

4.b - Modular mission types


4.c - Modular disciplinary analysis methods


36

Figure 2-13 shows one of the major deficiencies in the systems that have been

reviewed. Only Model Center and pyOPT have the capability to add new hardware, mission

types, and disciplinary analysis methods. The other systems require significant source

code modifications to adapt to new problems. ACSYNT is an early attempt at a ‘problem-

flexible’ system, consisting of disciplinary analysis modules created at NASA Langley

integrated through an early Model Center framework; however, it is no longer in use. AVDS,

in contrast, has the ability to integrate a stand-alone methods library into a synthesis

system. However, it has a fixed process that cannot be easily adapted as requirements

change.

Figure 2-14 Prioritization of Technology Development Efforts

Figure 2-14 shows that FLOPS, Model Center, PrADO and pyOPT allow the user

to adjust the level of disciplinary fidelity based on the given problem. This is a significant

attribute because as the problem advances to later stages in the design cycle life-cycle,

there is need to increase fidelity of the solutions in order to provide guidance to the design

team. As previously discussed, Huang emphasized the importance of a Database

Management System for Conceptual Design synthesis. Table 2-8 shows the data

management survey criterion for evaluating the synthesis systems and Table 2-9 shows

the results. Model Center meets all the requirements of a good DBMS except the

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Yes

No

Yes

No

Computer Based

5.a - Able to match hardware technology disciplinary models to problem requirements

By-Hand

37

connection to a Knowledge base system. pyOPT also has good database management

characteristics because of its object oriented programming design.

Table 2-8 Data Management Survey Criterion

Table 2-9 Data Management Capability

a

b

c

d

e

f

g

h

i

j

k

l

m

n

Provides completeness/error checks and data warnings

Easy to create, change, delete, and view projects and project data.

Accommodates all project types and project information

Supports entry of annotative comments and appending documents, images, and links for project

documentation

Data Management Criterion

Accommodates hundreds/thousands of projects

Supports data import from your existing systems and databases

Supports data export to your existing systems and databases

Supports dependency links among projects

Provides data cut-and-paste, project cloning, and data roll-over

Allows multiple portfolios and portfolio hierarchies (parent-child l inks)

Allows dynamic portfolios (portfolios defined based on latest project data)

Provides search, fi lter, and sort

Provides data archiving

Provides statistical analysis of historical data (e.g., trend analysis)

AAA

1991

ACSYNT

1987

ASAP

1974

AVDS

2010

FLOPS

1980

Model

Center

PrADO

1986

pyOPT

2012

VDK/HC

2000

a Yes Yes Yes Yes Yes Yes Yes Yes Yes

b No No No No No Yes No Yes No

c No No No No No Yes No Yes No

d Yes Yes Yes Yes Yes Yes Yes Yes Yes

e No Yes No No No Yes No Yes No

f No Yes No No No Yes No Yes No

g No No No No No Yes No No No

h No No No No No Yes No Yes No

i Yes Yes Yes No No Yes Yes Yes Yes

j No No No No No Yes No No No

k No No No No No Yes No No No

l No No No No No Yes No No No

m Yes Yes Yes Yes Yes Yes Yes Yes Yes

n No No No No No No No No No

4 of 14 6 of 14 4 of 14 3 of 14 3 of 14 13 of 14 4 of 14 9 of 14 4 of 14

38

Figure 2-15 Problem Input Characterization

Figure 2-15 shows that all of the systems reviewed do not provide any

methodology for requirements definition. This is because all these systems are designed

to proceed from the point after the requirements have already been established. The

outcome of this disintegrated approach to the problem definition is the lack of a feedback

loop between the problem definition and the problem solution. This eliminates the ability of

the decision maker to assess if a problem should be solved, or if the problem definition in

itself has been ill-formed. The solution to this problem requires an understanding of

acquisition approaches and the required flexibility to adapt synthesis systems to them.

2.2.3 Concepts for Advanced Synthesis Systems

The previous section indicated the deficiencies in representative aerospace

forecasting methodologies that are either too rigidly tailored to a specific type of problem,

or they are too open ended without providing any guidance on how problems should be

solved. Krishnamoorthy (2000) identifies 3 synthesis approaches that can benefit from

utilizing the Class V synthesis elements of KBS, DBS and Method Libraries proposed by

Chudoba (2001), Huang (2006) and Coleman (2010):

Synthesis by Problem Decomposisiton-Solution Recomposition – this involves the

reduction of the design problem into the lowest level of abstraction for analysis.

-1

0

1

Co

rnin

g 1

97

9

Ho

we

20

00

Jen

kin

son

19

99

Loft

in 1

98

0

Nic

ola

i 20

10

Ray

me

r 2

00

6

Ro

skam

19

85

Sch

aufe

le 2

00

0

Stin

ton

19

98

Tore

nb

ee

k 1

98

2

Wo

od

19

63

AA

A 1

99

1

AC

SYN

T 1

98

7

ASA

P 1

97

4

AV

DS

20

10

FLO

PS

19

80

Mo

de

l Ce

nte

r

PrA

DO

19

86

pyO

PT

20

12

VD

K/H

C 2

00

0

Ye

sN

oY

es

No

Computer Based

6.a - Methodological problem requirements

By-Hand

39

Then the individual solutions are recomposed into coherent whole solutions. This

approach relies heavily on understanding of the problem and ability to represent

them using blocks in the KBS and DBS.

Synthesis by Case-Based Reasoning – This involves using knowledge and

solutions of past cases and examples to solve present design problems. The

success of this method depends heavily on the availability of similar problems in

the KBS and DBS system.

Synthesis by Transformation Approach – This involves using rules and

generalizations from previous design studies to draw conclusions and solutions on

a current problem. This system relies on the DBS and KBS System having similar

problems converted into rules that can be matched to the current problem.

A modification of the synthesis paradigm has been initiated by utilizing the problem

decomposition-solution recomposition approach as the primary hypothesis for the current

research undertaking. This approach is flexible to adapt to the range of design problems

whilst offering uncompromised transparency due to the decomposition blocks and strategy.

2.3 Portfolio Planning for Technology Acquisition

The remedy to the dichotomy between the conceptual design tasks during the

product development lifecycle and the stakeholder requirements definition during the

acquisition lifecycle can be broken down into two parts. The first is the determination of a

methodology for assessing stakeholder interests and resources. The second is the creation

of a conceptual design methodology that is compatible with the acquisition solutions. Oza

(2016) addresses the earlier with project portfolio management, while, Gonzalez answers

the later with custom synthesis tool composition. This research is a bridge between these

two solution concepts.

40

2.3.1 Portfolio Planning Management

Portfolio refers to a company’s body of projects, ideas, technologies and

resources. In order to make decisions on the direction a company should follow, it is

important to (1) take stock of the company portfolio and the knowledge attached to them,

and to (2) prioritize the portfolio elements available for the given acquisition problem. Oza

proposes the use of Project Portfolio Management to bridge the gap between the

conceptual design and the stakeholder requirements definition of the acquisition lifecycle

(Oza 2016). Matthews (2010) explains that “… the project portfolio is focused on execution

and delivery, the innovation portfolio concerns itself with the development of a coherent

portfolio strategy and the maturation and selection of project candidates …”. Merkhofer

(2015) adds that project portfolio management involves the evaluation, prioritization and

selection of new projects in addition to the acceleration, reprioritization and termination of

existing projects in order to allocate or reallocate resources to maximize productivity.

Figure 2-16 shows an example of the Project Portfolio structure which allows streamlining

of resources, analysis capabilities and technology options over the acquisition lifecycle.

Figure 2-16 Comparison of Program vs Portfolio Approach to R&D (Janiga, 2014)

41

Oza (2016) postulates a semantically composable modeling platform system for

managing the following steps shown Figure 2-17:

1. Required inputs are identified and communicated by the acquisition researcher.

2. Inputs are processed and decomposed following the data logic strategy in the

system architecture.

3. The product portfolio model (PPM) is formulated into a technology portfolio to the

appropriate level of abstraction for the problem and is used to data-mine/assess

the portfolio’s performance.

4. The required developmental and technology risk tables (DTRts) are retrieved by

the PPM from a DBMS and managed according to the rationale in the inference

engine.

5. The DTRts library has been previously generated and used to describe the

capability performance model (CPM) for the technology portfolio.

6. Outputs are used to decide the processes to initialize for product sizing with the

zero silo DBMS approach.

Figure 2-17 Problem Formulation Data Automation Process (Oza 2016)

DTRt

Library

To zero silo

DBMSSystem

Architecture

Product

Portfolio Model

Capability

Performance Model

Integration

of Design

Results

42

The advantage of the PPM methodology is that it allows for low value and minimal

initial datasets associated with most concepts [to] be addressed by keeping early-phase

evaluations cheap and fast to minimize expenditures. In addition, the interface

methodology can assess each hardware technology independently, utilizing any level of

system abstraction with the aim to prioritize technology development to handle by

qualitative and quantitative metrics. Finally, the portfolios can be linked to company or

external databases systems and knowledge-base systems to provide additional insights

related to the problem at hand, such as supply chain information.

2.3.2 Synthesis Tool Composition

Gonzalez (2016) specifies a synthesis tool generation framework that is

compatible with the project portfolio management solution (Gonzalez 2016). The

framework utilizes syntactic and semantic composability principles to tailor make synthesis

systems to user problem requirements. Syntactic composability ensures that the

composition components can be and are connected properly as shown in Figure 2-18.

Semantic composability addresses whether the composed models are meaningful in terms

results and problem applicability. The synthesis tool generator achieves syntactic

composability by using a database management system to automatically generate

interfaces between the selected synthesis system composable components. It partially

enforces sematic composability by using a Graphical User Interface (GUI) that coordinates

the selection of meaningful composable components while, it is able to flag potential

modeling deficiencies.

43

Figure 2-18 Notional Example of Composability (Petty and Weisel 2003)

The advantage of the AVDDBMS methodology is its flexibility to both model old

design problems and adapt to new requirements. In addition, it takes advantage of the

lessons learned from many years of studying synthesis systems and leverages

advancements in computing technology to mitigate identified deficiencies. This has allowed

the creation of a system that is flexible to respond to stake holder requirement alternative,

structured to give new users guidance, quick to allow re-evaluations as new problem

information is received whilst being transparent enough to provide the decision-maker

confidence. This research contributes by defining the basic pieces of the AVDDBMS.

2.4 Chapter Summary and Solution Concept Specification

The project portfolio management methodology and the was invented as a

collaborative effort between Omoragbon (2016), Gonzalez (2016) and Oza (2016). The

original contribution of this author’s writing to these efforts is the definition of the building

44

blocks for each system, as well as the establishment of the data relationships between

them. This leads to the following solution concept specifications. The solution concept is

required to

utilize the concept of problem decomposition and system recomposition for

problem solving;

define building blocks for modeling aerospace vehicles as complex

multidisciplinary systems;

define building blocks for composable synthesis tool generation architecture for

aerospace vehicle conceptual design;

define building blocks for the project portfolio management architecture for

aerospace technology acquisition assessment;

Identify interfaces between the composable system architecture and the project

portfolio management architecture.

45

Chapter 3

COMPLEX MULTIDISCIPLINARY SYSTEM DECOMPOSITION CONCEPT

The objective of this research endeavor is to bridge the gap between aerospace

technology acquisition problems and aerospace vehicle conceptual design parametric

sizing. This involves the merger of considerations for stakeholder requirement definition

and an already complicated conceptual design synthesis processes. The stakeholder

requirements determine what aerospace design problems to solve, while the conceptual

design is concerned with determining the feasibility of the possible solutions to the design

problem. Chapter 1 discussed the problem that current technology acquisition analyses

tend to be qualitative and do not consider the added physical insight that parametric sizing

provides. Chapter 2 showed that typical conceptual design synthesis tools have been

developed to solve predefined design problems and are not easily adaptable to changing

stakeholder requirements. In order to leverage the existing knowledge and techniques from

stakeholder technology portfolio management together with a state-of-the-art existing

synthesis system, a composable technology innovation and sizing architecture has been

created in conjunction with (Oza 2016) and (Gonzalez 2016).

The technology innovation and sizing architecture is the culmination of research

done at the Aerospace Systems Engineering (ASE) Laboratory at the University of Texas

at Arlington. This research, amongst others, have been motivated by the following:

assessment of technologies for advancing commercial transports (Chudoba,

2009a);

evaluation of trust vectoring technologies (Chudoba 2009b; Omoragbon, 2013);

performance sizing of electric aircraft technologies (Chudoba. 2011);

creation of a high-speed technology investment databases (Haney 2013;

Chudoba, 2015);

46

technical feasibility assessment of a novel rotorcraft technologies (Chudoba,2014),

solution space screening of an air-launched hypersonic demonstrator (Chudoba,

2015b).

Figure 3-1 shows an overview of the primary steps defining the framework. The first step

is the decomposition of existing technologies and synthesis systems into building blocks.

The next step is the composition of these blocks into system models as given acquisition

or design problems require. The final step is the exploration of the different system models

for the best solutions to the problem.

The original contribution of the current research by the author Omoragbon to the

innovation and sizing framework is the prescription of the decomposition methodology and

the identification of the building blocks required for this framework to function. This chapter

discusses the CMDS decomposition solution concept for combining technology acquisition

with conceptual design.

Figure 3-1 Technology Innovation and sizing framework

3.1 Decomposition into CMDS Blocks

Complex Multidisciplinary System (CMDS) is the primary block managing data-

logic in the technology innovation and sizing Framework. In Chapter 2, the concept of

KEY RESULT: The AVD DBMS capability is rooted in three core complex multidisciplinary system (CMDS) integration technologies AVD delivers a technology innovation capability where the performance of the tool is tailored to the needs of the stakeholder and problem requirements

1

Complex Multidisciplinary System Decomposition

2

Complex Multidisciplinary System Synthesis

3

Complex Multidisciplinary System Data-mining

Objective

Measure the performance of candidate multidisciplinary systems

Prioritize candidate multidisciplinary systems with potential to contribute to stakeholder and problem requirements

Objective

Build candidate complex multidisciplinary systems to satisfy product development requirements

Draw relationships between candidate multidisciplinary system options and disciplinary effects

Objective

Systematically decompose the product, process, and method components for a specific multidisciplinary system and designed to solve a specific problem

Simplify the data requirements to consistently store in a database management system

SYSTEMAVD Sizing

SYSTEMHAVOC

SYSTEMVDK / HC

G

METHODS

PRODUCT

PROCESS

G

METHODS

PRODUCT

PROCESS

G

METHODS

PRODUCT

PROCESS

DECOMPOSED SYSTEMS

COMMONCOMPONENTS

SPECIFICCOMPONENTS

DBMSDATA ENTRY

Time, Cost, Uncertainty, …

47

viewing aerospace vehicles as complex multidisciplinary systems has been discussed. The

importance of CMDS is that it provides a common foundation for any object that can be

observed or modeled for any purpose. Finger and Dixon (1989a, 1989b) categorize two

considerations for modeling in design: (1) the description of the attributes of the design

artifact, and (2) the description of how the design artifact is designed. In order to account

for complexities arising from analysis by the CMDS from multiple disciplines, the second

consideration can be further divided into the analysis process and disciplinary methods

used for analysis.

Figure 3-2 shows CMDS decomposition blocks. The product block represents the

object that is to be studied. The analysis process block prescribes the major steps to

following evaluating the product. The disciplinary methods block describes the application

of disciplinary principles or empirical data to obtain results for the different steps in the

analysis process. This CMDS representation is consistent with both technology acquisition

and conceptual design problems. In acquisition problems, technology assessment

processes and methods are used to determine which products are best to pursue. For

example, as discussed in Chapter 1, the TRL calculator is a tool used determine

technologies that are the furthest along in development. In conceptual design problems,

synthesis tools and methodologies are used to either determine the performance of given

technologies or determine the vehicle size required for the technology to achieve a given

performance level. For example, Coleman (2010) discusses the analysis processes and

methods that different conceptual design synthesis systems use in designing aerospace

vehicles. The identification and documentation of the CMDS decomposition blocks

provides the tools necessary to quickly understand how both acquisition and conceptual

designs can be solved. The following sections discuss the decomposition of these CMDS

blocks.

48

Figure 3-2 CMDS Decomposition Blocks

3.2 Decomposition into Product Blocks

The product block describes the physical characteristics of the artifact that is to be

designed or acquired. There are three considerations that describe the product: (1) what it

does, (2) when it does it, and (3) the limitations or requirements for its operation. Figure

3-3 shows the product decomposition blocks based on these descriptions. Functional

subsystem decomposition is one of the common means of product decomposition in the

literature. However, operational events and operational requirements are introduced here.

The product decomposition block is important to both, acquisition assessment and

conceptual design. It creates a template description of technologies that are to be acquired.

It also defines the parameters of the object that is to be designed.

Figure 3-3 Product Block Decomposition

Product

Functional Subsystem

Operational Event

Operational Requirement

Mission Type

Flight Profile

Speed Range

Altitude Range

Regulations

Specifications

Lift Sources

Stability & Control Devices

Thrust Sources

n-Function

Thrust Sources

49

3.2.1 Subsystem Decomposition

Product decomposition in literature involves reducing the artifact into a hierarchical

network of subsystems and attributes until it reaches a desired level of abstraction

conducive for solving a given problem (Krishnamorthy 1996). For example, consider an

aircraft as the product shown in Figure 3-4. One of the subsystems is a wing and one of

the wing’s subsystems is a spar. If the problem requires the stress analysis of the aircraft,

the aircraft can be decomposed further into individual elements. Attributes are then used

to describe the properties of that subsystem at a given level of abstraction. In the previous

example, attributes could include the shape of the element and its material. As discussed

in Chapter 2 and (Shashank 2011, 2014), complexity increases with the number of levels

of abstraction and the type of analysis done at each level. It is imperative to increase the

levels of abstraction only as needed. However, there is a risk of loss of information about

the subsystem. In order to preserve subsystem information, subsequent levels of

abstraction can be stored as attributes. For example, if the problem only requires the

aerodynamic analysis of the wing, one of its attributes can be the type of wing sweep

(forward or backward).

Figure 3-4 Example of hierarchal product decomposition

Aircraft

Wing

Spar

Element

50

There are two major product decomposition schemes; the (1) structural or form

decomposition, and the (2) functional or modular decomposition. Structural decomposition

involves breaking the domain into various physical components that are used to construct

the solution (Krishnamoorthy, 2000). In order words, it is the decomposition of the product

according to its physical form. Figure 3-5 shows an example of the structural decomposition

of an aircraft. The benefit of structural decomposition is that it preserves the representation

of dependencies between assemblies, subassemblies and parts. Every node represents

an assembly level while every line represents a dependency. One drawback of structural

decomposition is that it may not produce a consistent representation across similar product

types. For example, the structural representation of aircraft with wing mounted engines is

different from that fuselage aft-mounted engines. The product decomposition scheme used

in this research is the functional decomposition scheme discussed in the next section.

Figure 3-5 Example of structural decomposition

3.2.1.1 Functional Decomposition

Functional decomposition, on the other hand, involves the resolution of the product

into constituent parts based on the primary functions they serve. In this scheme, the

primary functions at each abstraction level are identified and the subsystem that satisfy

these functions are grouped into modules. Figure 3-6 shows an example of functional

decomposition of an aircraft. In this decomposition scheme, the engine is not a sub

Aircraft

FuselageWing Assembly

WingLanding Gear

Turbofan Engine

Tail Assembly

Vertical Tail

Horizontal Tail

51

assembly of the wing assembly. It is a subsystem of its own because it serves a separate

function. The benefit of functional decomposition is that it gives a consistent representation

of similar product type and shows points that can benefit from standardization and

interchangeability. The drawbacks, however, are that information about the assembly of

the product is lost in this representation and subsystems with multiple functions may not

be properly represented.

Figure 3-6 Example of functional decomposition

Functional decomposition is a popular technique in literature. It is a primary step

in the system engineering process (DAU 2013, SMC 2010; USAF 2011; OSD 2015; MSFC

2012) and analysis of large complex systems (Courtois, 1985). It can be used to arrive at

product families based on modularity (Martin and Ishii 1997) and component

standardization (Kota and Sethuraman 1998), customer demands (Gonzalez–Zugasti et

al. 1998, 1999). Advanced methods have been developed for configuration design, based

on functional decomposition using design structure matrices (Newcomb et al. 1998),

Artificial Intelligence (AI) (Soininen et al., 1998), constraint satisfaction (Mittal &

Falkenheiner, 1990), resources (Heinrich & Jungst, 1991), evolutionary methods (Gero et

al. 1997; Rosenman & Gero, 1997), rewrite rules methods (Agarwal & Cagan, 1998;

Agarwal et al., 1999), heuristic rule methods (Kolodner, 1993), fuzzy neural network

(Kusaik and Huang 1996), design negotiation (Kusaik et al 1996) and design guided

methods (Corbett and D.W. Rosen 2004).

Aircraft

Lift:Wing

Landing:Landing Gear

Thrust:Turbofan

Compression:Fan, Inlet, Compressor

Combustion:Burner

Expansion:turbine, Nozzle

Volume:Fuselage

Stability & Control:Tail

52

3.2.1.2 Applicability to Acquisition and Conceptual Design

The functional decomposition scheme has been adopted for this research because

it is conducive to both acquisition and conceptual design problems. Company technology

portfolios are typically based on some representation functional modules. That means, this

scheme provides a line of connection to data-bases and knowledge-bases in order to

support decision-making. In addition, technology assessment methods described in

Chapter 1, such as the TRL calculator, can be used on each module at that different levels

of abstraction. Finally, new product families can be generated from combining modules by

using the many configuration generation techniques available. Conceptual design benefits

because the scheme provides a template for judging the applicability of a synthesis tool to

a given design problem. In addition, it helps to identify analysis gaps that should be

addressed in the flexible synthesis system by showing which parts of the product the tools

do not analyze.

Table 3-1 shows some major subsystem functions for aerospace vehicles and

some hardware examples. Hardware is used interchangeably with functional subsystem

because for aerospace vehicle conceptual design, subsystems are physical hardware not

abstract forms such as software modules or sub-functions. Although some hardware

functions are related to traditional disciplines, such as aerodynamics and propulsion, the

subsystems are not chosen based on discipline (e.g. aerodynamic subsystems). This is

because functions describe as the cause’ why subsystems exist, while disciplines assess

the ‘effects’ of having them as a consequence. For example, even though engines are

providing thrust, they have aerodynamic effects such as drag.

53

Table 3-1 Description of Hardware Function Categories

Function Purpose of Hardware Example(s)

Drag Source Provide drag force Parachute, Autogyro, etc

Landing System Provide capability to land/recover Tricycle Gear, Skids, etc

Lift Source Provide lift force Wing, Wing Flap, Lifting Body, etc

Stability & Control Provide stability and/or control Aileron, Elevon, etc

Thermal Protection Provide thermal protection Ablator, Heat Shingle, Heat Pipe, etc

Thrust Source Provide thrust force Turbojet, Turbofan, Scramjet, etc

Volume Supply Supply internal volume Fuselage, Fuel Tank, Pod, etc

54

Figure 3-7 Functional Subsystem Block Decomposition

55

3.2.1.3 Hardware Attributes Definition

Figure 3-7 shows a functional decomposition tree for aerospace conceptual

design. The number of functions and subsystems can vary depending on the design

problem. In this research, one level of abstraction has been chosen in order to reduce the

complexity of the conceptual design problem. That is, only one level of decomposition is

used. As previously mentioned, there is risk of loss of information about subsequent levels

in this scheme. In order to avoid this loss, hardware attributes have been defined to

preserve detail information about the subsystem. Table 3-2 shows some examples of

hardware attributes that can be used.

Table 3-2 Hardware Attribute Examples

Function Hardware Attribute Attribute Options

Lift Source Body Waverider Features Yes, No

Nose Bluntness Sharp, Spherical Bluntness

Nose Shape Cone, Spatula

Under Body Round Bottom, Flat Bottom

Cross Section Circular, Elliptic, Rectangular, FDL-8H

TPS Material T.D. NiCr, Radiation Shingle (Superalloys), Carbon-Carbon, TUFI/AETB, Haynes, BLA-S, BLA-HD, BRI-16, FRSI, SIP

Structure Material Tungsten, Inconel, Titanium, Aluminum, Steel, Composite hot structure

Wing Waverider Features Yes, No

Planform Trapezoidal Tapered, Delta, Double Delta, Cropped Delta

Dihedral Dihedral, Anhedral, gull, Inverted gull, Channel, Cranked

Position High, Mid, Low

Aspect Ratio High, Mid, Low

Sweep Back Swept, Forward Sweep, No Sweep

TPS Material T.D. NiCr, Radiation Shingle (Superalloys), Carbon-Carbon, TUFI/AETB, Haynes, BLA-S, BLA-HD, BRI-16, FRSI, SIP

Structure Material Tungsten, Inconel, Titanium, Aluminum, Steel, Composite hot structure

56

Thrust Source Scramjet Inlet 2-D Planar, 3-D Inward Turning

Isolator 2-D, 3-D, 2-D to 3-D

Combustor 2-D, 3-D

Nozzle SERN, 3-D, 2-D

RamJet Inlet 2-D Planar, 3-D Inward Turning

Combustor 2-D, 3-D

Nozzle SERN, 3-D, 2-D

⁞

3.2.1.4 Hardware Shell and Implementation Blocks

Hardware shells and implementations have been defined to reconcile conceptual

design products and acquisition products. Conceptual design subsystem attributes do not

need to be well defined or based on existing products. They can be conceptual and

analyzed as long as the abstractness of the model is acceptable. The shell hardware

representation is a designation for hardware without defined attributes. They can be

combined together to form different shell vehicle configurations without the constraint of

matching attributes. Figure 3-8 shows 6 different shell vehicle packages created 10 shell

hardware.

Figure 3-8 Example Shell Vehicle Packages

Lift Source All-Body All-Body Blended Body Blended Body Wing Body Wing Body

Stability & Control X-Tail X-Tail Twin Tail &

Elevons

Twin Tail &

Elevons

Twin Tail &

Elevons

Twin Tail &

Elevon

Thrust Source SCRAMjet SCRAMjet SCRAMjet SCRAMjet SCRAMjet SCRAMjet

Landing System Tricycle Parachute Tricycle Parachute Tricycle Parachute

Thermal Protection

System

Passive Passive Passive Passive Passive Passive

Shell

Package #5

Shell

Package #6

Shell

Package #4

Function Shell

Package #1

Shell

Package #2

Shell

Package #3

57

In contrast to conceptual design subsystems, acquisition subsystems need to be

based on existing or well defined technologies. This is because the key performance

parameters of cost, time and uncertainty are easier to determine for products with historical

backing. Hardware implementation is the designation for subsystems based on existing

technologies. Hardware implementations can be created by decomposing existing vehicles

or identifying subsystems from public domain to proprietary or secret literature. Figure 3-9

shows the decomposition of X-51A into hardware implementations. The Hytech engine is

the resulting implementation of scramjet hardware. Subsequent analysis using this

implementation can assume TRL levels, costs, supply chain information and other

attributes of the Hytech engine. Another application of hardware implementation is the

combination with shell vehicle packages to create innovative portfolios of candidate

vehicles. For example, the shell vehicles shown in Figure 3-8 can be permutated with

hardware implementations for each shell hardware to form new results.

Figure 3-9 X-51A decomposition into hardware implementations

3.2.2 Operational Event Decomposition

Operational events describe how the product is designed to be used. They are the

characteristics of the product that explain how it behaves overtime. Operational events are

Stability and Control Function

Lift Source Function

Thrust Source Function

• all moving fins

• bottom tail dihedral

• spatula nose

• flat bottom

• rectangular cross section

• Boeing light-weight ablator (sprayed-on)

• Boeing light-weight ablator (honeycomb

reinforced)

• Boeing reusable insulation

• strain isolation pad (under tiles)

• 2D planar inlet

• RTR isolator

• rectangular combustor

• rectangular cross section

X-Tail

Passive TPS

All Body

SCRAM-Jet

Hardware Hardware Implementation Hardware Attributes/Components

Thermal Protection Function

HyTech Engine

X-51A

FunctionVehicle

BLA-S

BLA-HD

BRI-16 Tile

FRSI

SIP

58

used to define how the product performance should be simulated. For aerospace vehicles,

the operational events define the vehicle mission. The decomposition of the aircraft mission

into flight segments is common practice in flight mechanics texts (Vinh, 1995; Philips, 2010;

Stengel 2004; Miele, 1962), flight trajectory simulation codes (Powell, 2003; Paris et al,

1996) and trajectory optimization techniques (Vinh, 1981). They are used for stability

investigations, control law designs, performance estimations, flying and handling qualities

evaluation of aircraft. Figure 3-10 shows the decomposition blocks used in this research.

Figure 3-10 Operational Event Decomposition Block

59

3.2.2.1 Mission Type

The mission type describes the vehicle objective of the vehicle based on its start

and end points in space. Table 3-3 shows the basic mission types, descriptions and some

vehicle examples. Point-to-point (P2P) missions involve the vehicle flying from one point

on earth to another point on earth. The design objective of this mission could either be for

the vehicle to meet a design range or a design endurance (mission duration). In a suborbital

mission, the vehicle flies to a design altitude outside the earth’s atmosphere. However, its

maximum velocity is not enough to sustain orbit. For orbital insertion missions, the vehicle

flies from earth into a design orbit defined by an insertion altitude, velocity and flight path

angle. Conversely, reentry missions begin at an altitude, velocity and flight path angle in

orbit then end on the earth surface. Finally, in escape missions, the vehicle exits the

atmosphere and reaches escape velocities required to leave the earth gravitational field.

Table 3-3 Description of Mission Types

Type Objective of Vehicle Example Vehicles

Point-to-Point Move vehicle or payload from one point to another B747, A320, F22, C-5

Sub Orbital Reach space (>100 km) without sufficient energy to complete one orbital revolution

Spaceship 2

Orbital Insertion Reach space (>100km) with sufficient energy to remain at a specific altitude for more than one orbital revolution

Saturn V, Falcon 9

Orbital Reentry Enter from orbital altitude through planet’s atmosphere Apollo Capsule, Dragon Capsule

In-Space Perform mission objectives in planetary orbit ISS

Escape Provide sufficient energy to escape planetary gravity well Voyager 1&2

3.2.2.2 Flight Profile

The flight profile describes the path the vehicle takes between the start and end

points defined by the mission type. The flight profile is decomposed into separate flight

phases for further analysis (Vinh, 1981). These flight phases or segments form blocks that

can be used to build various flight profiles. Figure 3-11 shows example flight profile of a

60

two-place advanced deep interdictor aircraft. It is a P2P mission with both, a combined

range and endurance objective.

Figure 3-11 Example Flight Profile (Jenkinson, 2003)

3.2.2.3 Function Modes and Flight Segments

Not all functional subsystems serve their functions for the entirety of the operation

of the product. In some cases, products switch between multiple subsystems during their

operational phases. Function modes are introduced to describe the different ways each

function is satisfied during product operation. Table 3-4 shows example thrust source

modes for a vehicle with a rocket for acceleration and a scramjet for cruise. Function modes

61

can also be used to describe configuration changes. Consider the XB-70 shown in Figure

3-12. It droops it wings during supersonic flight to reduce wave drag by morphing into a

waverider, ultimately riding the shock wave for compression lift. This change in

configuration can be modelled using two lift source modes. The first to represent the

drooped wing for supersonic flight phases. The second to represent the regular

configuration for the other (mostly low-speed) flight phases. Modes can be defined for

every function to simulate various scenarios including engine-out conditions, landing gear

deployment and dual mode scramjet ramjet-to-scramjet mode transition.

Table 3-4 Example thrust function modes for a vehicle

Function Mode Hardware in Use Flight Segment Description

Thrust Source Mode 1 Rocket Ascent/Climb Rocket used for acceleration

Thrust Source Mode 2 Rocket, Scramjet Transition Transition between rocket and scramjet

Thrust Source Mode 3 Scramjet Cruise Scramjet used for cruise

Thrust Source Mode 4 None Glide Engines off for glide

Figure 3-12 Artist rendering of North American XB70 (Bagera, 2008)

3.2.2.4 Speed and Altitude decomposition blocks

The speed and altitude decomposition blocks define the environment the vehicle

will experience during its flight. As discussed in Chapter 2, the environment significantly

62

affects the complexity of the vehicle design. Molecular dissociation at hypersonic speed,

thermal soak during reentry and ultraviolet radiation at high altitudes are some of the

phenomenon that occur at extreme environments. The altitude and speed range building

blocks are meant to give a representation of flow phenomenon the vehicle is expected to

encounter. Mach number flow regimes shown in Table 3-5 are used as the speed range

decomposition blocks. And the decomposition blocks for the altitude range are the Earth

atmospheric layers shown in Table 3-6. In both cases, multiple selections can be made

according to the expected flight profile and mission type of the vehicle.

Table 3-5 Mach Number Flow Regimes

Mach Regime Mach Number

Subsonic <0.8

Transonic 0.8-1.0

Sonic 1.0

Supersonic 1.0-5.0

Hypersonic 5.0-10.0

High Hypersonic >10.0

Table 3-6 Earth atmospheric layers used as altitude range blocks

Earth Atmospheric Layers Altitude Range

Exosphere 700 to 10,000 km (440 to 6,200 miles)

Thermosphere 80 to 700 km (50 to 440 miles)

Mesosphere 50 to 80 km (31 to 50 miles)

Stratosphere 12 to 50 km (7 to 31 miles)

Troposphere 0 to 12 km (0 to 7 miles)

3.2.3 Operational Requirement Decomposition

Operational requirements describe regulations, limits and boundaries on the

utilization of the products. Figure 3-13 shows the operational requirement decomposition

blocks. Regulatory bodies impose standards that must be met in order to maintain safety

of operation. For aircraft, these regulations include airworthiness standards and noise

63

standards such as Federal Aviation Regulation (FAR) 23 (US 1900) for general aviation

and FAR 25 (US, 1900) for commercial transport. Other operational requirement

considerations are specifications that dictate non-functional or mission design choices that

define the product. Examples include human rating, propellant type, pollution limits etc.

The inclusion of operational requirements in the definition of CMDS do complete the holistic

description of the product. They help in acquisition problems by giving a means to

communicate organizational design philosophies and stakeholder requirements to the

designer. They also provide additional parameters that differentiate competing

technologies.

64

Figure 3-13 Operational Requirement Decomposition Blocks

3.3 Decomposition into Analysis Process Blocks

The Analysis Process Block is the second branch of the CMDS decomposition. It

shows the coordination of analysis activities and procedures during for the design of the

product. The number and types of interactions between activities described by analysis

process are good indicators of product complexity. Process decomposition is a means to

manage the complexity of the product design (Johnson and Benson, 1984) and business

processes (Huang et. Al., 2010). The design structure matrix is a powerful tool developed

to represent processes during decomposition (Steward, 1981). Figure 3-14 shows an

example of a design structure matrix. The flow of information from the column index to the

row index is marked with “*” while “+” shows a diagonal. After decomposition, design

processes can be improved using techniques such as partitioning algorithm (Rogers, 1989;

Sobieszczanski-Sobieski, 1982, 1989), Cluster Identification algorithms (Kusiak and Chow,

1987) and Branch-and-Bound algorithm (Kusiak and Cheng 1990). They can help identify

events that can run concurrently, minimize number of overlapping parts, decrease resource

usage and maximize measures of effectiveness.

Figure 3-14 Design Structure Matrix representation of a process (Kusiak 1999)

1 2 3 4 5 6 7 8

1 + * * *

2 + *

3 + *

4 * * + *

5 * * +

6 +

7 +

8 * +

6

3

1 2

4

7

5

8

65

Figure 3-15 shows the analysis process decomposition blocks used in this

research. It is applicable to CMDS decomposition for both acquisition and conceptual

design problems. The blocks have been chosen based on the principle that decomposition

process modules should have high cohesion within themselves, but low coupling with other

modules (Kusiak, 1999). They are also based on the description of conceptual design

synthesis systems processes identified by Coleman (2010). There are two parts to the

decomposition: (1) System Elements, and (2) Disciplinary Elements.

Figure 3-15 Analysis Process Decomposition Blocks

3.3.1 System Analysis Blocks

The system analysis blocks describe the formulation of the overarching problem.

Analysis problems can be posed as function evaluations, system of equation solutions or

objective function optimizations. These types of problems can be constructed from a set of

independent and dependent variables. A dependent variable is selected from any output

of a module (step) in the process. An independent variable is selected from inputs to

process modules which are not outputs from other modules. The objective function block

66

is used to create relationships between independent and dependent variables which

specify the type problem (evaluation, solution or optimization).

3.3.2 Disciplinary Analysis Blocks

The Disciplinary Analysis Blocks describe the overarching steps in the analysis

process. In this research, the analysis process is constructed such that each process step

is a module containing all the analysis performed by a single discipline. Discipline, in this

context, refers to a formal branch of knowledge where the scientific method is used to

generate empirical data or theories to explain phenomena that are observed. The choice

to isolate disciplinary analysis to modules is based on the principle by Kusiak (1999), that

decomposition process modules should have high cohesion within themselves but low

coupling with other modules. A result of a disciplinary analysis is termed ‘disciplinary effect’.

Effects are represented as variables in the process. For example, total drag is a disciplinary

effect evaluated by the aerodynamic discipline and it can be represented as D. The

disciplinary effects block is used to define effects that are key performance parameters,

inputs to other disciplines or used as dependent variables in the objective function.

Disciplinary dependencies blocks are input parameters that define the degrees of freedom

of a disciplinary analysis module. For example, if the aerodynamics module dependencies

are altitude, velocity and angle of attack, then all aerodynamic effects are only applicable

to 3DOF simulations. The analysis process formulation is intentionally decoupled from the

product, so that it does not contain any product information.

3.4 Decomposition into Disciplinary Method Blocks

Disciplinary methods are sets of empirical, numerical or analytical functions or

equations used to determine disciplinary effects of a product or its subsystems. Disciplinary

methods populate the disciplinary modules defined in the analysis processes. Figure 3-16

67

shows the three aspect of disciplinary methods identified in this research effort. The

product model block uses the hardware, mission and operation decomposition blocks

explained in Section 3.2. As opposed to product decomposition, where the entire product

is described, the product model blocks describe only parts of the product and their relation

to given method models. The variables block contains variables used as method

dependencies (inputs), as method effects (output) or method constraints. Constraint

variables describe quantifiable ranges of applicability of a method. For example, a Mach

number range, 0.8<M<1.0, expresses the boundaries of a transonic aerodynamics method.

The analysis block contains the computation details of the method. The discipline describes

the formal branch of knowledge from which the method is created. The assumptions are

qualitative concessions made during the derivation of the analysis equations.

Figure 3-16 Disciplinary Method Block Decomposition

68

3.5 Chapter Summary

This Chapter discusses the Complex Multidisciplinary System Decomposition

(CMSD) solution. The CMDS is the first of a three step process in the composable

technology innovation and sizing architecture. The architecture created in conjunction with

(Oza 2016) and (Gonzalez 2016) leverages existing knowledge and techniques from

stakeholder technology portfolio management as well as existing aerospace synthesis

systems. The CMDS decomposition blocks represent a generic means to manage system

complexity. The product decomposition blocks, analysis process blocks, decomposition

blocks and disciplinary method blocks are the main parts of the CMDS decomposition

scheme identified, developed and software-implemented in this research. The biggest

benefit of the formulation presented is the fact that it enables both, aerospace technology

acquisition portfolio management and conceptual design parametric sizing tool

customization.

69

Chapter 4

COMPLEX MULTIDISCIPLINARY SYSTEM DECOMPOSITION TOOL

The Complex Multidisciplinary System Decomposition (CMSD) concept described

in Chapter 3 is designed for use in a database management framework. The prototype

Database management framework developed to this end is the AVDDBMS. It has been

created in conjunction with Oza (2016) and Gonzalez (2016) for the purpose of validating

the proposed innovation technology and parametric sizing solution concept. The

decomposition branch of this framework allows the user to systematically decompose

CMDS for improving understanding and storing CMDS attribute for later reuse. AVDDBMS

has three layers: (1) Graphical User Interface (GUI) layer, (2) database layer, and (3)

analysis layer. Table 4-1 shows the software and corresponding programming languages

comprising AVDDBMS. Figure 4-1 shows the 3 layer architecture. The GUI Layer is the

means by which the user interacts with AVDDBMS. It is created using MS Access forms that

initiate VBA commands that control the database. The database layer contains SQL

commands which manage data transfer between the GUI and database. It is also used to

generate custom CMDS synthesis tools. The analysis layer, in the MATLAB environment,

is where the CMDS synthesis tools are executed. This chapter discusses the CMDS

decomposition methodology as well as CMDS decomposition tools available in the

AVDDBMS framework.

Table 4-1 AVDDBMS Layers

Layer Software Programming Language

GUI Layer Microsoft Access Microsoft Visual Basic with Applications (VBA)

Database Layer Microsoft Access Search Query Language (SQL)

Analysis Layer MATLAB MATLAB Script

.

70

Figure 4-1 AVD DBMS Three Layer Architecture

4.1 CMDS Decomposition Methodology

Complex Multidisciplinary Systems (CMDS) Decomposition is a powerful tool for

defining the technology acquisition portfolio and evaluating the applicability of a conceptual

design synthesis system to a design problem. As discussed in Chapter 2, a CMDS is any

body of knowledge aimed at describing or analyzing a system. That is, it has a broad range

of embodiments. Reference texts that discusses technologies and parametric sizing tools

for vehicle synthesis are CMDS examples. Figure 4-2 shows the decomposition

71

methodology for the aerospace CMDS developed in this context. First, a given CMDS is

decomposed into product, process and method blocks. Then these blocks are examined

to identify common components that already exist in the database, followed by specific

components that are unique to the CMDS. Finally, all the CMDS information is stored using

AVDDBMS. Using this methodology, the understanding of existing problems and capability

to solve new problems increase as more CMDS are decomposed.

Figure 4-2 Methodology for CMDS Decomposition

4.2 CMDS Decomposition Input Forms

AVDDBMS decomposition input forms are used to execute the aerospace CMDS

decomposition methodology. There are three decomposition forms: (1) product input form,

(2) analysis input form, and (3) disciplinary method input form.

4.2.1 Product Input form

Figure 4-3 shows the product input form. It is a card that describes the CMDS

product decomposition blocks described in Section 3.2. The top half represents the

72

hardware, mission (operational event) and operational requirement blocks. Hardware are

grouped by function, mission is grouped by altitude range, mission type, speed, and flight

segment. The operational requirements are human rating, propellant type, regulations and

other non-hardware specifications. The bottom half of the card is used to map hardware to

function modes and function modes to flight segments as discussed in Section 3.2.2.3.

Function modes are different ways a group of hardware are used to satisfy a function. For

example, thrust modes for aircraft with both rockets and scramjets are: (1) all engines on,

(2) rocket only, (3) scramjet only, (4) all engines off. The trajectory segment mapping is

used to specify when each function mode is used. For example, all engine off is used for

the glide segment.

Figure 4-3 Product Input Form

73

4.2.2 Analysis Process Input Form

Figure 4-4 shows the analysis process input form. This card is used to record the

CMDS analysis decomposition block information described in Section 3.3. The form has 5

sections: (1) System Variables, (2) Discipline, (3) Disciplinary Process Variable, (4) Error

Function, and (5) Dependent Variable Check. System variables consist of independent

variables and dependent variables which are selected from all the variables available in

the database. They are variables that are used to specify the objective function as

discussed in Section 3.3.1. The discipline section is used to specify the disciplines involved

in the analysis process as well as the order they are run. The disciplinary process variable

section dictates the key effects of each discipline. For each discipline, only variables that

are known to be outputs of that discipline can be selected. The error function section shows

the objective function of the analysis process. Finally, the dependent variable check section

ensures the dependent variables selected in the system variable section are actually

dependent. That is, variables that are marked as disciplinary effects and are directly used

in the objective function.

74

Figure 4-4 Analysis Process Input Form

4.2.3 Disciplinary Method Input Form

Figure 4-5 shows the disciplinary method input form. This card has 9 sections for

describing CMDS disciplinary method decomposition blocks discussed in Section 3.4. (1)

The first section is the reference section. It is used to keep track of page numbers of all

references used in constructing the method. It is also linked to the AVDDBMS reference

library for more detail. (2) The method information gives the details of the method such as

discipline, method ID, title date created and date update. (3) The ‘more button’ on that

section provides additional detail about the method including assumptions made. (4) The

input section displays variable inputs used by method, while, the (5) output section is for

disciplinary effects (variable outputs) calculated in the method. The (6) constraint section

specifies quantitative limits of method applicability. The (7) hardware mission (operational

event) and (8) operation sections are used to describe aspects of the product that is

modeled by the method. Finally, the (9) analysis section links the method card to the

75

analysis platform. AVDDBMS uses the MATLAB environment for analysis. Therefore, the

method analysis steps are written in a MATLAB script as shown in Figure 4-6.

Figure 4-5 Disciplinary Method Input Form

76

Figure 4-6 Example Methods Library Entry MATLAB m-file (AERO_MD0001.m)

One key to the AVDDBMS implementation is the re-structuring of analysis file data

input and output requirements. When writing a new analysis file for a new method, it is not

necessary to include the description of any input variables in the analysis file. Any new

analysis method is made with the assumption that any input variable that has been selected

using the disciplinary method input form exists in the workspace for that file. This means

that when writing a new method file, it is only necessary to include lines of code dealing

with the analysis meant to be performed. In other words, the burden of tracking where input

variables have been created, or how they are connected in the system, is not placed on

the user/creator of the method but rather the onus is on the system itself to correctly track

and implement these connections.

4.3 Chapter Summary

This chapter discussed the implementation of the CMDS decomposition

methodology. The AVDDBMS is the tool developed for this effort. It is a database

management software programmed in MS Access and the MATLAB environment using

VBA, SQL and MATLAB script languages. CMDS decomposition is executed using the

decomposition forms available in AVDDBMS. The forms include product input, process input

and disciplinary method input forms. CMDS have a broad range of embodiments ranging

77

from reference texts to parametric sizing tools. As more CMDS are decomposed, both

understanding of existing problems and capability to solve new ones increase.

78

Chapter 5

COMPLEX MULTIDISCIPLINARY SYSTEM DECOMPOSITION CASE STUDY

5.1 Case Study Objectives

The objective of this research has been to bridge the gap between technology

acquisition and conceptual design. The CMDS decomposition methodology developed has

been identified as a vital piece creating the bridge between the two domains. The purpose

of this case study is to demonstrate the application of this methodology to a relevant

acquisition and design problem. The problem selected is the solution space screening

study of air-launched hypersonic demonstrators. It was done in 2015 in conjunction with

Bernd Chudoba, Lex Gonzalez, Amit Oza under the Summer Faculty Fellowship Program

(SSFP) at Wright-Patterson Air Force Base. The case study problem required the

contribution of this research thesis as well as (Oza 2016) and (Gonzalez 2016). This

chapter discusses the problem, research strategy, strategy execution and study results.

5.2 Case Study Problem – AFRL GHV

The US Air Force Science and Technology Research plan (USAF, 2011) highlights

US interests in hypersonic technologies. These technologies enable operational high-

speed weapons and aircraft platforms that are vital increasing warfighting capabilities. They

also contribute to intelligence, surveillance, reconnaissance and other mission objectives.

The Air Force projects these systems to be operational by 2030 (Norris, 2012). In order to

ensure the success of these systems there is a need to determine the best high-speed

technologies. Improper planning has been the bane of many research projects in the past

as expressed by former Undersecretary of the Air Force, Brockway McMillian

(Morgenthaler, 1964):

79

We don’t spend enough time, energy, or talent in deciding how to deploy our technological resources - - in other words, in deciding what to develop out of the products of our research. Just as our research and development program must match the risks that we face in the international arena, so also must our planning of that program be commensurate with the commitments we are making. …How much effort should we expend to be sure we are committing these resources toward a product that we really need and one that we can really use?

Another hindrance to hypersonic vehicle research is its sensitive nature to national

defense. International Trade in Arms Regulations (ITAR) guidelines and industry

proprietary technology considerations have restricted the amount of information that can

be accessed and published in academia. However, the benefit of collaboration between

multiple bodies to solve these problems is unquestionable. In response to this need, the

Air Force Research Laboratory (AFRL) has provided a setting for collaboration between

aerospace hypersonic research partners working in government, industry or academia.

This avenue for collaboration is the generic hypersonic vehicle (GHV) study. Ruttle (2012)

describes the study thus:

Due to proprietary or ITAR restrictions, AFRL cannot readily provide most data or designs to researchers who are not in the US Government or associated contractor community. It was decided that a family of in-house designs should be created which would be publicly releasable and relevant to current hypersonic projects. AFRL would then be able to share these designs and any data derived from them with other government, academic or industry partners and thereby foster greater collaboration within the area.

The objective of this study was to create a family of generic hypersonic vehicles (GHV) completely in-house using design tools either owned by or licensed to AFRL. The GHV would have to be based upon the state of the art in hypersonic engine design so that it would be valuable for studies of operability, controllability, and aero-propulsion integration. It was agreed early on that the vehicle would need to have a blended wingbody configuration, 3D inlet and nozzle, an axisymmetric scramjet combustor, and a metallic structure with a thermal protection coating. The GHV would cruise at Mach 6 within a dynamic pressure range of 1000 to 2000 psf, and maneuver at a maximum loading factor of approximately 2G.

80

For the sake of the current research endeavor, the GHV study is posed as an

acquisition and conceptual design problem. Figure 5-1 shows the configuration and

mission description of the GHV study. The case study questions to be answered are:

Acquisition: What relevant technology packages satisfy the GHV type mission?

Acquisition: What technology package gives the least acquisition risks?

Conceptual Design: What is the configuration of the least risk technology package?

Conceptual Design: What is the technical feasibility of the technology package?

Figure 5-1 Generic Hypersonic Vehicle Study Description (Ruttle et al., 2012)

81

5.3 Case Study Research Strategy

The strategy adopted balances the acquisition and conceptual design challenge.

The acquisition challenge is to determine the technologies to invest in. The conceptual

design challenge is to evaluate the technical feasibility of the technology investments and

address identified pitfalls. Primary emphasis has been placed on the utilization of existing

hypersonic vehicle knowledge at the UT Arlington Aerospace Systems Engineering (ASE)

Laboratory and execution of the technology innovation and sizing framework developed in

conjunction with Gonzalez (2016) and Oza (2016). This involves the prioritization of a

hypersonic demonstrator vehicle portfolio and producing parametric flight vehicle technical

feasibility solution spaces. The study is executed in two parts:

Part 1: Technology Acquisition Portfolio Prioritization

Part 2: Conceptual Design Parametric Sizing

5.4 Part 1: Technology Acquisition Product Portfolio Prioritization

The initially wide-open trade space of hypersonic technologies is refined

successively and constrained to trades of immediate relevance to the GHV type mission.

The range of past-to-present hypersonic demonstrators is assessed in order to deliver a

small, yet reasonable number of design attributes directly addressing the immediate critical

mission-operation-hardware path or need. The overall trade-space consists of

combinations of: (a) mission (endurance, payload, speed), (b) operation (carrier system,

booster system, landing system), (c) hardware (thrust source: 2D and 3D scramjet; lift

source: blended-body and wing-body). Figure 5-2 shows the technology acquisition

portfolio prioritization road map. The strategy is summarized as

Reference vehicle identification from AVD Database;

Reference vehicle decomposition into technology portfolio;

82

Candidate vehicle composition from technology portfolio;

Candidate vehicle portfolio assessment;

Project vehicle selection.

Figure 5-2 Technology Acquisition Portfolio Prioritization Roadmap (Chudoba, 2015)

5.4.1 Reference Vehicle Identification from AVD Database

A primary literature search has been conducted to identify relevant past and

present data and knowledge related to the planning and development of hypersonic

technology demonstrators. A systematic literature survey, has been an ongoing effort

throughout the existence of the ASE Laboratory. Source for accessing normal and radical

design data and knowledge have been (a) public domain literature, (b) organization internal

sources, and (c) expert advice. For efficient handling of design related data and

information, a dedicated computer-based aircraft conceptual design data-base (AVD-DB)

has been developed (Haney 2016). This system handles disciplinary and inter-disciplinary

literature relevant for conceptual design (methodologies, flight mechanics, aerodynamics,

etc.), interview-protocols, flight vehicle case study information (descriptive-, historical-,

numerical information on conventional and unconventional flight vehicle configurations),

83

simulation and flight test information, etc. The overall requirement for the creation of the

AVD-DB has been simplicity in construction, maintenance, and operation, to comply with

the underlying time constraints.

The system has been used to identify a listing of reference vehicle with

technologies relevant to the GHV type mission. The vehicles chosen have suitably

selected, structured, and condensed flight vehicle conceptual design data and information.

The research goal, to develop an air-breathing hypersonic technology demonstrator

requires accounting for as many design-related interactions as necessary, since the

rationale for the evolution of aircraft is diverse as a quick browse through aviation history

reveals. In order to decrease the overall risks towards a failing acquisition program, it is

important to select technologies with successful track records or high potential. Figure 5-3

shows the reference vehicle list selected from the AVD-DB. The feasibility era represents

vehicles that have had previous engineering and/or flight test success, while the maturation

era identifies vehicles with potential towards the next generation of hypersonic flight

demonstrators.

.

Figure 5-3 Vehicle Decomposition – Reference Vehicle Listing

X-43 [1994 – 2004]X-51 [2005 – 2013]

X-15 [1954 – 1970]

FEASIBILITY ERA MATURATION ERA

HYFAC 221 [1970]

X-24C L301 [1977]

HIFiRE 6

GHV

Falcon HTV-3x

+

84

5.4.2 Reference Vehicle Decomposition into Technology Portfolio

The reference vehicles are used to build the technology portfolio for acquisition

assessment. Figure 5-4 shows the decomposition methodology for defining the technology

portfolio. This methodology is based on the product decomposition blocks defined in the

current research undertaking. The primary focus of the portfolio being defined is technology

hardware. The primary hardware function types used to define the portfolio are: (1) lift

source, (2) stability & control device, (3) thrust source, (4) landing system, and (5) thermal

protection System (TPS). AVDDBMS is utilized to decompose the reference vehicles into

their constituent hardware technologies. Figure 5-5 shows the resulting technology

portfolio. Table 5-1 shows the attributes of the lift and thrust source technologies.

Figure 5-4 Reference Vehicle Decomposition Methodology

85

Figure 5-5 Reference Vehicle Decomposition into functional hardware

Function Hardware

GH

V

X-2

4C

-L3

01

HY

FAC

22

1

San

ger

II

X-4

3 C

X-5

1A

Cru

ise

r

HIF

iRE

6

Falc

on

HTV

-3x

Tota

l

All-Body x x x 3

Blended Body x x x 3

Wing Body x x 2

Twin Tail x x x x x 5

X-tail x x x 3

Elevons x x x x x 5

Rocket x 1

Ramjet x 1

SCRAMjet x x x x x 5

DMSJ 0

Turbo-Ramjet x x 2

Tricycle Gear x x 2

Chute x 1

Active 0

Passive x x x x x x x x 8

Thrust

Source

Landing

System

Thermal

Protection

Lift Source

Stability &

Control

86

Table 5-1 Lift and Thrust Source Technology Portfolio Attributes

Function HW Attribute GHV X-24C-L301 HYFAC 221 X-43 C X-51A Cruiser HIFiRE 6 HTV-3x

Lift Body Waverider No No No No No No No

Nose Blunt Spherical Spherical Sharp Sharp

Nose Shape Cone Cone Spatula Spatula

Under Body Flat Round & Flat Flat Bottom Flat Bottom Flat Bottom Flat

Cross Section Circular FDL-8H Elliptic Rectangular Rectangular Circular

TPS Material T.D. NiCr, radiation shingle

Carbon-Carbon, TUFI/AETB,

Haynes

BLA-S, BLA-HD, BRI-16, FRSI, SIP

Structure LockAlloy Aluminum Tungsten, Steel,

Aluminum

Tungsten, Inconel, Titanium, Aluminum, Steel,

Composite hot structure

Wing Waverider Yes No Yes No

Planform Cropped Delta Cropped Delta Cropped Delta Double Delta

Position Low Low Low Low

Aspect Ratio Low Low Low Low

Sweep Swept Back Swept Back Swept Back Swept Back

Structure LockAlloy

Thrust Scramjet Engine Name HyTech HyTech

Inlet 3-D Inward

Turning 2-D 2-D Planar 2-D Planar

3-D Inward Turning

Isolator 3-D 2-D 2-D 3-D

Combustor 3-D 2-D 2-D 3-D

Nozzle 3-D 2-D 2-D 2-D 3-D

Ramjet Engine Name MA145

Inlet 2-D

Combustor 3-D

Nozzle 3-D

Rocket Engine Name LR-105, LR-101

87

5.4.3 Candidate Vehicle Composition from Technology Portfolio

Although the portfolio of technologies has merit in itself, the desired product of the

acquisition study is a prospective hypersonic vehicle demonstrator. Consequently, these

technologies need to be combined in a syntactical sound manner to compose vehicles.

The resulting vehicles are candidates for acquisition. Figure 5-6 shows the methodology

for composing vehicles. The concept of shell vehicles for aerospace vehicle portfolio

definition is discussed in Section 3.2.1.4. The shell vehicles are used to define vehicle

configurations without attribute details. After the definition of these shells, the technology

portfolio is combined with the shell vehicles to form candidate vehicles. This process is

automated using AVDDBMS as described by Oza (2016). Table 5-2 shows the resulting shell

vehicles, while, Table 5-3 and Table 5-4 show the final candidate vehicle portfolio.

Figure 5-6 Candidate Vehicle Synthesis Methodology

88

Table 5-2 Technology Portfolio Definition Shell Vehicles

Table 5-3 Candidate Vehicle Portfolio

Lift Source All-Body All-Body Blended Body Blended Body Wing Body Wing Body

Stability & Control X-Tail X-Tail Twin Tail &

Elevons

Twin Tail &

Elevons

Twin Tail &

Elevons

Twin Tail &

Elevon

Thrust Source SCRAMjet SCRAMjet SCRAMjet SCRAMjet SCRAMjet SCRAMjet

Landing System Tricycle Parachute Tricycle Parachute Tricycle Parachute

Thermal Protection

System

Passive Passive Passive Passive Passive Passive

Shell

Package #5

Shell

Package #6

Shell

Package #4

Function Shell

Package #1

Shell

Package #2

Shell

Package #3

X-24C L301TPS Concept












89

Table 5-4 Candidate Vehicle Portfolio (Cont’d)

5.4.4 Candidate Vehicle Portfolio Value Assessment

The candidate vehicle portfolio defined consists of vehicles with both, high risk and

low risk technologies. The acquisition problem objective is to maximize technology

investment value while minimizing risks. Figure 5-7 shows the portfolio value assessment

methodology described by Oza (2016). It is a combination of the technology acquisition

techniques described in Chapter 1. It involves the integration of the Technology Readiness

Levels (TRL), Integration Readiness Levels (IRL) and Advanced Degree of Certainty (ADC)

into the technology portfolio to facilitate the composition of candidate vehicle TRL-ratings

and risks. In order to manage uncertainties that may exist, best case, worst case and most













90

likely scenarios are considered. Figure 5-8 shows the candidate vehicle portfolio

assessment results. Although candidate vehicles CV6 and CV10 have the highest

composite TRLs, CV6 has a lower variance in TRL. (Oza, 2016) provides more detail about

the portfolio assessment result.

Figure 5-7 Portfolio Value Assessment Methodology

Figure 5-8 Composite Candidate Vehicle TRL for risk scenarios

5.4.5 Project Vehicle Selection

CV10 has been selected as the most desirable technology combination to develop

into a flight vehicle project. This selection is based on candidate portfolio value assessment

1 2 3 4 5 6

KEY RESULT: Assess overall portfolio value and risk trends and sensitivities to measure technology performance and potential for growth

1

Zoom Out

Technology Sensitivity Analysis Technology Scenario-based Evaluation Portfolio Performance

1 2 3

Candidate Vehicle

Hardware Component: Data Generation

Hardware Component Families: Data Generation

Technology Readiness Level

Advancement Degree of

Difficulty


Advancement Degree of

Difficulty

+


X Integration Readiness Level

Candidate Vehicle

Acquisition Scenarios: Data Generation

Acquisition Scenarios Matrix: Data Generation

MOST LIKELY CASE VALUES


Integration Readiness Level

Adv. Degree of Difficulty

System Readiness Level

Manufacturing Readiness Level

WORST CASE VALUES




BEST CASE VALUES




1 2 3 4 5 6 7 8 9

AD

D IR

L T

RL

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9

Ad

van

cem

en

t D

egr

ee

of

Dif

ficu

lty

to T

RL=

6

Current Candidate Vehicle TRL

2500

2499

2498

2497

2496

2495

2494

2493

2492

2491

2490

2489

2488

2487

2486

2485

2484

2483

2482

2481

2480

2479

2478

Unacceptable – Too Risky Acceptable DesirableToo well Known for Adv.

Tech Demonstrator

Technology Risk Envelope

Technology Risk Overview

2476

2477

2478

2479

2480

2481

2482

2483

2484

2485

2486

2487

2488

2489

2490

2491

2492

2493

2494

2495

2496

2497

2498

2499

2500

2501

2.5 3 3.5 4 4.5 5

Can

did

ate

Ve

hic

le ID

Candidate Vehicle TRL

ML IRL

WC IRL

BC IRL

23.9%

30.9%

20.4%

34.9%

31.8%

20.7%

29.2%

26.2%

26.0%

30.9%

30.9%

29.9%

17.6%

25.1%

22.0%

23.1%

27.3%

27.3%

22.2%

21.6%

27.3%

31.8%

24.2%

28.5%

-17.7%

-18.0%

-16.8%

-20.9%

-17.0%

-14.7%

-18.2%

-14.3%

-13.9%

-25.8%

-22.7%

-13.9%

-14.1%

-21.7%

-20.8%

-13.3%

-25.1%

-22.2%

-20.8%

-16.8%

-25.9%

-17.8%

-25.3%

-17.6%

-40% -20% 0% 20% 40%

CV15

CV03

CV11

CV17

CV18

CV16

CV21

CV22

CV24

CV13

CV01

CV04

CV12

CV23

CV07

CV20

CV09

CV05

CV19

CV08

CV14

CV02

CV10

CV06

% Change in TRL

Impact of BC and WC Scenarios

41.6%

48.9%

37.2%

55.9%

48.8%

35.5%

47.3%

40.5%

39.9%

56.7%

53.6%

43.7%

31.7%

46.8%

42.8%

36.4%

52.4%

49.5%

43.1%

38.4%

53.2%

49.6%

49.5%

46.0%

0% 20% 40% 60%

CV15

CV03

CV11

CV17

CV18

CV16

CV21

CV22

CV24

CV13

CV01

CV04

CV12

CV23

CV07

CV20

CV09

CV05

CV19

CV08

CV14

CV02

CV10

CV06

% Change in TRL

Variation due to BC and WC Scenarios

2.91

3.04

3.05

3.08

3.09

3.13

3.16

3.17

3.21

3.23

3.23

3.26

3.28

3.30

3.33

3.36

3.39

3.39

3.44

3.55

3.60

3.60

3.77

3.77

0 1 2 3 4

CV15

CV03

CV11

CV17

CV18

CV16

CV21

CV22

CV24

CV13

CV01

CV04

CV12

CV23

CV07

CV20

CV09

CV05

CV19

CV08

CV14

CV02

CV10

CV06

ML Candidate Vehicle TRL

Candidate Vehicle TRL Comparison

91

and other considerations. Although CV6 has equal TRL and lower TRL variance than

CV10, the choice of landing system hardware was the deciding factor. A landing gear

system as opposed to a recovery parachute is perceived to be more conducive for vehicle

reuse. Figure 5-9 shows the characteristics of CV10 and the potential feasibility space that

should be explored.

Figure 5-9 Characteristics of Candidate Vehicle 10

5.5 Part 2: Conceptual Design Parametric Sizing

CV10 has been chosen for a follow-on conceptual design study based on the

results of the technology portfolio value assessment. The conceptual design sequence

consists of three individual phases executed in sequence: (1) parametric sizing (PS), (2)

configuration layout (CL), and (3) configuration evaluation (CE) (see Figure 2). For the

demonstration of AVDDBMS, only parametric sizing is executed aimed at exploring the

feasible trade space for the technology acquisition study. The sequence of the parametric

sizing study is organized as follows:

Resulting Trade Volume

Hardware

• Thrust Source[ 2-D Scramjet ]

• Lift Source

[ Blended Body]

Mission

• Endurance Time [ 10 – 30 min ]

• Payload

[ 0 – 1,500 lbs ]• Speed

[ Mach 5 – 8 ]

Operational Requirements

• Carrier Vehicle [ F-15 – B-52 ]

• Booster

[ External ]• Landing Option

[Runway ]

~TRL: 3.77

ΔTRL: 49.5%

92

Decomposition of synthesis systems at the ASE Laboratory;

Composition of sizing CMDS for CV10;

Visualization of CV 10 feasibility solution space.

5.5.1 Decomposition of Synthesis Systems at ASE Laboratory

The purpose of the synthesis system decomposition is to increase the AVDDBMS

sizing capability by populating it with products, processes and methods from existing

synthesis systems. The ASE Laboratory has access to a range of synthesis systems based

on years of building a parametric process library (Coleman 2010). Figure 5-10 shows one

of the synthesis systems available, AVDS. It is a best-practice design-to-mission sizing

process capable of first-order solution space screening of a wide variety of conventional

and unconventional vehicle configurations (Coleman 2010). AVDS relies on a robust

disciplinary methods library for analysis and a unique multi-disciplinary sizing logic and

software kernel enabling data storage, design iterations, and multi-disciplinary process

convergence. Figure 5-11 and Figure 5-12 show a snapshot of the products, analysis

process and method libraries populated by using the AVDDBMS decomposition forms

discussed in Section 4.2.

Figure 5-10 Overview of AVDS Synthesis System (Coleman 2010)

AVDsizing

Weight budget: compute OWEw

Volume budget: compute OWEv

Iterate Spln until OWEw and OWEv converge

Iterate for each t specified

Iterate over any independent design

variable

Geometry

Constraint Analysis: T/W=f(W/S)

Trajectory:

ff=f(trajectory,aero,propulsion)

Fundamental Sizing Steps

Sizing LogicOEW esitmationTrajectory

Anlaysis

Convergence

Logic

Constraint

Anlaysis

Constraints

Take-off

Approach

speed

(W/S)TO

Feasible solution

space

Cruise

Aborted Landing OEI

2nd

Segment Climb OEI

Current Design Point

Current (W/S)TO

Required

(T/W)TO

Trajectory

93

Figure 5-11 Design Mapping Matrix of AVDDBMS Product Library

**A

dd N

ew

**

HY

FA

C 2

00

HY

FA

C 2

01

HY

FA

C 2

04

HY

FA

C 2

05

HY

FA

C 2

06

HY

FA

C 2

07

HY

FA

C 2

10

HY

FA

C 2

11

HY

FA

C 2

12

HY

FA

C 2

13

HY

FA

C 2

14

HY

FA

C 2

20

HY

FA

C 2

21

HY

FA

C 2

31

HY

FA

C 2

32

HY

FA

C 2

33

HY

FA

C 2

34

HY

FA

C 2

50

HY

FA

C 2

51

HY

FA

C 2

52

HY

FA

C 2

53

HY

FA

C 2

54

HY

FA

C 2

55

HY

FA

C 2

56

HY

FA

C 2

57

HY

FA

C 2

70

HY

FA

C 2

71

HY

FA

C 2

80

HY

FA

C 2

81

HY

FA

C 2

82

HY

FA

C 2

84

HY

FA

C 2

85

HY

FA

C 2

90

HY

FA

C 2

91

HY

FA

C 2

92

VA

B A

ir-launched H

ydro

gen

VA

B A

ir-launched H

ydro

gen 2

Qs

VA

B A

ir-L

aunched K

ero

sene

VA

B E

xpendable

Booste

r H

ydro

gen

VA

B E

xpendable

Booste

r K

ero

sene

All Body 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Wing Body 1 1 1 1 1 1 1 1 1

Thermal Protection Heat Shingles 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

** Ramjet ** 1 1 1 1 1

** RamScramjet ** 1 1 1 1 1 1 1 1 1 1 1 1 1

** Rocket ** 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

** Turbojet ** 1 1 1 1 1 1

** TurboRamjet ** 1 1 1 1 1 1 1

Volume Supply ** Integral Tank ** 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Air Launch 1 1 1 1 1 1 1 1 1 1 1 1

Booster Launch 1 1 1 1 1 1 1

Horizontal Takeoff 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Vertical Takeoff 1 1

Constant Q Climb 2 1 2 1 1 1

Constant Altitude Acceleration 1 1 1 1

Constant Mach Endurance Cruise 1 1 1 1 1

Constant Mach Range Cruise 1

Gliding Descent 1 1 1 1 1 1

Air Snatch 1

Parachute Landing 1 1 1 1

Powered Landing 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Unpowered Landing 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Subsonic 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Supersonic 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Transonic 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Hypersonic 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Mission Type Point-to-Point 1 1

Manned 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Unmanned 1 1 1 1 1 1 1 1

Aerozine 50 (Liquid) 1 1 1

Hydrogen (Cryogenic Liquid) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

JP-5 (Liquid) 1 1 1 1 1 1 1 1 1

Air (N/A) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Nitrogen Tetroxide (Liquid) 1 1 1

Oxygen (Cryogenic Liquid) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Product

Operational

Requirements

Product DMM

Human Rating

Fuel (Storage State)

Oxidizer (Storage

State)

Mission Speed

Functional

Subsystems

Operation

Events

Mission Segment

Thrust Source

Lift Source

ProductBlocks

Product List

Product Attributes

94

Figure 5-12 Design Mapping Matrix of AVDDBMS Process and Method Librarires

**A

dd N

ew**

_AG

1

VA

B P

roce

ss

VA

B P

roce

ss_A

G6

VA

B P

roce

ss_A

G7

VA

B P

roce

ss_A

G8

SPLN 1 1 1 1 1

WS 1 1 1 1 1

ALT

AMACH

AOA

THRL_VAR

V

AERO 3 3 3 3 3

FLTCON 1 1 1 1 1

GEO 2 2 2 2 2

PM 5 5 5 5 5

PROP 4 4 4 4 4

WB 6 6 6 6 6

ACAP 2 2 2 2 2

AIP 6 6 6 6

AISP 4 4 4 4 4

AISTR 6 6 6 6 6

AKSTR 6 6 6 6

AKW 2 2 2 2 2

AKW0 2 2 2 2

AL 2 2 2 2 2

ALC_AL 2 2

ALT 4 4 4 4 4

AMACH 3 3 3 3 3

AOA 3 3 3 3 3

AOA 4 4 4 4 4

BPLN 2

CD 3 3 3 3 3

CL 3 3 3 3 3

CS_SPAT 2 2

FF 5 5 5 5 5

FT_AVAIL 4 4 4 4 4

FT_MAX_HW 5 5 5 5

HC_LC 2 2

OF 4 4 4 4

OWE_V 6 6 6 6 6

OWE_W 6 6 6 6 6

RANGE 5

SFSPLN 2 2 2 2

TAU 2 2 2 2

THETA1_RAMP 2 2

THRL_VAR 4 4 4 4 4

TOGW 6 6 6 6 6

V 4 4 4 4 4

WR 5 5 5 5 5

OWE_V - OWE_W = 0 1 1 1 1 1

WS - TOGW / SPLN = 0 1 1 1 1 1

Analysis Process

Process DMM

Discpline Run

Order

Disciplinary

Effects

Objective

Function

Independent

Variables

Disciplinary

DependenciesProcess Attributes

ProcessBlocks

Process List

AE

RO

_MD

0001

AE

RO

_MD

0002

AE

RO

_MD

0003

AE

RO

_MD

0004

FLT

CO

N_M

D00

01

GE

O_M

D00

01

PM

_MD

0001

PM

_MD

0002

PM

_MD

0003

PM

_MD

0004

PM

_MD

0005

PM

_MD

0006

PM

_MD

0007

PM

_MD

0008

PM

_MD

0009

PR

OP

_MD

0001

PR

OP

_MD

0003

PR

OP

_MD

0004

WB

_MD

0001

All Body 1 1 1 1 1

Wing Body 1 1 1

Thermal Protection Heat Shingles 1 1

** Ramjet ** 1

** RamScramjet ** 1

** Rocket ** 1 1

** Turbojet ** 1

** TurboRamjet ** 1

Volume Supply ** Integral Tank ** 1

Air Launch 1 1

Booster Launch 1 1

Horizontal Takeoff 1 1

Vertical Takeoff 1 1

Constant Q Climb 1

Constant Altitude Acceleration 1

Constant Mach Endurance Cruise 1

Constant Mach Range Cruise 1

Gliding Descent 1

Air Snatch 1

Parachute Landing 1

Powered Landing 1

Unpowered Landing 1

Subsonic 1

Supersonic 1

Transonic 1

Hypersonic 1 1

Mission Type Point to Point

Manned 1 1 1 1 1

Unmanned 1 1 1 1 1

Aerozine 50 (Liquid) 1

Hydrogen (Cryogenic Liquid) 1

JP-5 (Liquid) 1

Air (N/A) 1

Nitrogen Tetroxide (Liquid) 1

Oxygen (Cryogenic Liquid) 1

AERO 1 1 1 1

FLTCON 1

GEO 1

PM 1 1 1 1 1 1 1 1 1

PROP 1 1 1

WB 1

ACAP 1

AIP 1

AISP 1 1 1

AISTR 1

AKSTR 1

AKW 1

AKW0 1

AL 1

ALC_AL 1

ALT 1

AMACH 1

BPLN 1

CD 1 1 1 1

CL 1 1 1 1

CS_SPAT 1

FF 1 1 1 1 1 1 1

FT_AVAIL 1 1 1

FT_MAX_HW 1 1 1 1 1 1 1

HC_LC 1

OF 1 1 1

OWE_V 1

OWE_W 1

RANGE 1 1 1 1 1 1 1

SFSPLN 1

TAU 1

THETA1_RAMP 1

TOGW 1

V 1

WR 1 1 1 1 1 1 1 1

Product

(Functional

Subsystems)

Product

(Operation

Events)

Product

(Operational

Requirements)

Method DMM

Process

Key Effects

Disciplines

Oxidizer (Storage

State)

Lift Source

Thrust Source

Mission Segment

Mission Speed

Human Rating

Fuel (Storage State)

Method Blocks

Method List

Method Attributes

Process Domain Mapping Matrix Method Domain Mapping Matrix

Product Blocks

95

5.5.2 Composition of Sizing CMDS for Candidate Vehicle 10

Gonzalez (2016) describes the capability of AVDDBMS to compose the custom

sizing CMDS for given conceptual design problems. In the context of the 2015 SFFP study,

CV10 has been chosen for the parametric sizing study based on results from the

technology portfolio value assessment in Section 5.4. CV10 is based on the X-24C (L-301)

lift source technology and X51A thrust source technology. A literature search identified the

characteristic dimensions of the vehicles including, fuselage length, height and width,

planform, root/tip chord, sweep angle and incidence angle. Based on this understanding,

the AVDDBMS CMDS composition form is executed. The composition steps are

Matching disciplinary methods to the given product and process;

Selecting most appropriate disciplinary methods from the matched list;

Arranging selected product, process and methods into CMDS blue print;

Generating customized CMDS executable.

The CV10 project vehicle and the AVDS analysis process were input into the

AVDDBMS CMDS composition form as product and process respectively. Table 5-5 shows

the objective function for AVDS process. The AVDS process poses an equation solution

problem to determine the planform area, Spln, and Wing loading, W/S, required for operating

weight estimates based on volume calculations, (𝑂𝑊𝐸)𝑉, to equal operating weight

estimates based on weight calculations, (𝑂𝑊𝐸)𝑊. AVDDBMS provides a list of matching

disciplinary methods for the parametric sizing problem.

Process Blocks Variable / Problem Type Equation/Symbol

Independent Variable Planform Area, ft2 𝑆𝑝𝑙𝑛

Independent Variable Wing Loading, N/ft2 𝑊

𝑆=

𝑇𝑂𝐺𝑊

𝑆𝑝𝑙𝑛

Dependent Variable Operating Weight Based on Weights Budget, N

(𝑂𝑊𝐸)𝑉 =𝑊𝐹𝑖𝑥 + 𝑊𝐸𝑛𝑔 + 𝑊𝑇𝑃𝑆

11 + 𝜇𝑎

−𝑊𝑠𝑡𝑟

𝑂𝐸𝑊− 𝐹𝑊𝑆𝑦𝑠

+ 𝑊𝑝𝑎𝑦𝑙𝑜𝑎𝑑

96

Dependent Variable Operating Weight Based on Volume Budget, N

(𝑂𝑊𝐸)𝑉 =𝑉𝑇𝑜𝑡𝑎𝑙 − 𝑉𝑆𝑦𝑠𝑡𝑒𝑚𝑠 − 𝑉𝐸𝑛𝑔 − 𝑉𝑆𝑡𝑟 − 𝑉𝑇𝑃𝑆 − 𝑉𝑉𝑜𝑖𝑑

𝑊𝑅 − 1𝜌𝑝𝑝𝑙 ∗ 𝑔0

Objective Function Equation Solution Problem

(𝑂𝑊𝐸)𝑉 − (𝑂𝑊𝐸)𝑊 = 0

(𝑊

𝑆)

𝐺𝑢𝑒𝑠𝑠 −

𝑇𝑂𝐺𝑊

𝑆𝑝𝑙𝑛

= 0

Table 5-6 shows the disciplinary methods selected from the matching list of

method. In the arranging step, Aero Methods 5, 6, 7 were arranged to execute during the

subsonic, transonic-supersonic and hypersonic flight regimes respectively. Figure 5-13

shows a Design Structure Matrix (DSM) of the resulting CV10 sizing CMDS blueprint. It

describes the flow of information during the execution of the CMDS.

Table 5-5 AVDS Analysis Process Objective Function Block

Process Blocks Variable / Problem Type Equation/Symbol

Independent Variable Planform Area, ft2 𝑆𝑝𝑙𝑛

Independent Variable Wing Loading, N/ft2 𝑊

𝑆=

𝑇𝑂𝐺𝑊

𝑆𝑝𝑙𝑛

Dependent Variable Operating Weight Based on Weights Budget, N

(𝑂𝑊𝐸)𝑉 =𝑊𝐹𝑖𝑥 + 𝑊𝐸𝑛𝑔 + 𝑊𝑇𝑃𝑆

11 + 𝜇𝑎

−𝑊𝑠𝑡𝑟

𝑂𝐸𝑊− 𝐹𝑊𝑆𝑦𝑠

+ 𝑊𝑝𝑎𝑦𝑙𝑜𝑎𝑑

Dependent Variable Operating Weight Based on Volume Budget, N

(𝑂𝑊𝐸)𝑉 =𝑉𝑇𝑜𝑡𝑎𝑙 − 𝑉𝑆𝑦𝑠𝑡𝑒𝑚𝑠 − 𝑉𝐸𝑛𝑔 − 𝑉𝑆𝑡𝑟 − 𝑉𝑇𝑃𝑆 − 𝑉𝑉𝑜𝑖𝑑

𝑊𝑅 − 1𝜌𝑝𝑝𝑙 ∗ 𝑔0

Objective Function Equation Solution Problem

(𝑂𝑊𝐸)𝑉 − (𝑂𝑊𝐸)𝑊 = 0

(𝑊

𝑆)

𝐺𝑢𝑒𝑠𝑠 −

𝑇𝑂𝐺𝑊

𝑆𝑝𝑙𝑛

= 0

Table 5-6 Disciplinary methods selected for composing CV10 Sizing CMDS

Discipline Sizing Name Method Title Reference

Flight Condition FLTCON_MD0001 Atmospheric Model (MINZNER et al. 1959)

Geometry GEO_MD0002 Hypersonic Air-breather Geometry (AFRL SFFP CV10).

(Chudoba 2010)

Aerodynamics AERO_MD0005 Hypersonic Convergence Aerodynamic Estimation Method - Subsonic

(Czysz 2004; Sforza 2016)

AERO_MD0006 Hypersonic Convergence Aerodynamic Estimation Method - Supersonic


97

AERO_MD0007 Hypersonic Convergence Aerodynamic Estimation Method - Hypersonic


Propulsion PROP_MD0008 HAP Stream Thrust SERN CEA (C2H4 - Air) Look-Up Table

(Heiser and Pratt 1994)

Performance Matching

PM_MD0001 Gliding Decent at Max L/D (Miele 1962)

PM_MD0003 Constant Q-Climb to an Altitude and Velocity at Small Flight Path Angles

(Miele 1962)

PM_MD0008 Constant Mach Range Cruise at Small Flight Path Angles

(Miele 1962)

PM_MD0009 Launch Methods using WR (Miele 1962)

PM_MD0010 Steady Level Turn (Vinh 1981)

Weight & Balance

WB_MD0003 Convergence OWE Estimation for Scramjet w/ Landing skids

(Czysz 2004; Harloff 1988)

98

Figure 5-13 Design Structure Matrix for CV10 sizing CMDS blueprint

5.5.3 Visualization of Candidate Vehicle CV10 Feasibility Solution Space

5.5.3.1 Solution Space Description

The objective of parametric sizing is to identify and visualize the feasibility solution

space of vehicles that satisfy a particular mission. A solution space is a dashboard

visualization of vehicle metrics in order to aid in decision-making. It is constructed by

plotting resulting metrics of fixed-mission sized vehicles with prescribed vehicle parameter

GEO

_M

D0

00

2

AER

O_

MD

00

05

AER

O_

MD

00

06

AER

O_

MD

00

07

PR

OP

_M

D0

00

8

PM

_M

D0

00

1

PM

_M

D0

00

3

PM

_M

D0

00

8

PM

_M

D0

00

9

PM

_M

D0

01

0

WB

_M

D0

00

3

OW

E_V

- O

WE_

W

WS

- TO

GW

/ S

PLN

SPLN

WS

AK

W

AL

ALL

E

BP

LN

SF SFSP

LN

SPLN

_SF

SWET

TAU

TAU

_SF

VP

VTO

TAL

CD

CL

AIS

P

FT_

AV

AIL

OF

FF THR

L_V

AR

_M

AX

WR

AIP

AIS

TR

OW

E_V

OW

E_W

TOG

W

GEO_MD0002 ○ ← ←

AERO_MD0005 ○ ← ← ←

AERO_MD0006 ○ ← ← ← ← ← ←

AERO_MD0007 ○ ← ← ←

PROP_MD0008 ○ ← ←

PM_MD0001 ○ ← ← ← ← ← ← ←

PM_MD0003 ○ ← ← ← ← ← ← ←

PM_MD0008 ○ ← ← ← ← ← ← ←

PM_MD0009 ○ ← ← ← ← ← ← ←

PM_MD0010 ○ ← ← ← ← ← ← ←

WB_MD0003 ○ ← ← ← ← ←

OWE_V - OWE_W ○ ← ←

WS - TOGW / SPLN ○ ← ← ←

SPLN ○

WS ○

AKW ← ○

AL ← ○

ALLE ← ○

BPLN ← ○

SF ← ○

SFSPLN ← ○

SPLN_SF ← ○

SWET ← ○

TAU ○

TAU_SF ← ○

VP ← ○

VTOTAL ← ○

CD ← ← ← ○

CL ← ← ← ○

AISP ← ○

FT_AVAIL ← ○

OF ← ○

FF ← ← ← ← ← ○

THRL_VAR_MAX ← ← ← ← ← ○

WR ← ← ← ← ← ○

AIP ← ○

AISTR ← ○

OWE_V ← ○

OWE_W ← ○

TOGW ← ○

Dis

cip

linar

y O

utp

uts

Var

iab

les

Complex Multidisciplinary

System DSM Blueprint and

Data Relationships

Selected Methods

Objective

FunctionSe

lect

ed

Me

tho

dO

bje

ctiv

e

Fun

ctio

n

99

variation. Figure 5-14 shows a sample solution space. it is constructed from vehicle gross

weight + booster weight in the y-axis and the vehicle planform area in the x-axis. Each

point on the space represents a vehicle converged to satisfy the objective function defined

in the analysis process. This continuum map shows the effect of increasing cruise time, 𝑡,

vehicle slenderness parameter, 𝜏, and payload weight, 𝑊𝑝𝑎𝑦.

Constraint lines can be added to the solution spaces to provide more interpretation

with respect to constraints and limitations. Figure 5-15 shows a sample solution space

constrained by maximum weights allowable by various carrier vehicles. The F-15 limit is

the maximum weight for a vehicle carried underneath an F-15. The B-52 limit is for under

the wing of a standard B-52 while the NB-52 A/B limit is for a modified B-52 with a

structurally reinforced wing. The NB-52 A/B limit is grayed out as the vehicle is no longer

in service. Figure 5-16 shows a sample solution space constrained by length and span

limits for vehicles carried under B-52H wing. These solution spaces constraints discussed

define the feasibility boundaries for the parametric sizing study.

Figure 5-14 Sample fixed-mission solution space

10000

20000

30000

40000

50000

60000

70000

80000

90000

001 002 003 004 005 006 007 008

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (m^2)

CV_10 Solution Space

0.080.09

0.110.1

12 min.

16 min.

21 min.

9 min.

0 lb

1333 lb

2666 lb

4000 lb

10000

20000

30000

40000

50000

60000

70000

80000

90000

100 200 300 400 500 600 700 800

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (ft2)

Design Summary

tcruise 9 min

Range 1388 km 749 nm

TOGW 48637 N 10934 lbs

Wppl 10073 N 2265 lbs

OEW 38564 N 8670 lbs

Wbooster 57274 N 12876 lbs

t 0.08

S pln 18.6 m2201 ft2

b 3.36 m 11 ft

l 9.90 m 32 ft

L/D

cruise 4.25

Isp cruise1177 sec

Isp_eff

climb 1206.50 sec

100

Figure 5-15 Sample fixed mission solution space with carrier weight constraints

Figure 5-16 Sample fixed mission solution space with carrier packaging constraints

5.5.3.2 CV10 Sizing Solution Space Results

Figure 5-17 shows the design mission parameters used in sizing CV10. After

airdrop, the vehicle climbs at a constant dynamic pressure of 1,500 lb/ft2 untill it reaches

the cruise altitude for the design Mach number. The vehicle then cruises at design Mach

10000

20000

30000

40000

50000

60000

70000

80000

90000

001 002 003 004 005 006 007 008

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (m^2)


NB-52A/B Limit

22000B-52 Limit

0.080.09

0.110.1

12 min.

16 min.

21 min.

9 min.

X-24C

16000

F-15 Limit

0 lb

1333 lb

2666 lb

4000 lb

10000

20000

30000

40000

50000

60000

70000

80000

90000

100 200 300 400 500 600 700 800

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (ft2)

B-52 Length Limit

B-52 Span Limit

0 lb

1333 lb

2666 lb

4000 lb

30

40

50

60

70

80

90

5 10 15 20 25

Cru

ise

r L

en

gth

+ B

oo

ste

r L

en

gth

(ft

)

Cruiser Span (ft)

101

number for a specified endurance time, between 0 and 500 s. Afterwards, the vehicle

performs a 2 g turn towards base and cruises for the previously specified cruise endurance.

Finally, the vehicle glides at a constant altitude to decelerate to the equilibrium glide

velocity and equilibrium glides to landing altitude. The overshoot of base ensures that the

vehicle has sufficient fuel margin to return. Two design speeds, M6 and M7, have been

considered for this sizing study. Feasibility solution spaces are generated by varying cruise

time, 𝑡, vehicle slenderness parameter, 𝜏, and payload weight, 𝑊𝑝𝑎𝑦 for each design.

Figure 5-17 Design mission for CV10 solution space

Figure 5-18 and Figure 5-19 show the carriage weight feasibility space for a family

of M6 and M7 CV10 vehicles. These results show that all the booster mounted vehicles

are too heavy to be carried by existing carriage vehicles. Feasibility only exists if the B52H

is recommissioned or a new carriage vehicle is developed. Figure 5-20 and Figure 5-21

show the landing feasibility of the vehicle families. The results show that all M6 vehicles

can land on a runway, while all but the stout, 𝜏 = 0.11, M7 vehicles can safely land. Finally,

Figure 5-22 and Figure 5-23 show the carriage size feasibility solution spaces. These

results indicate that only some of the booster mounted M6 CV10 vehicles are small enough

to fit under the carrier vehicle wing (geometry limitation). It is shown that no M7 Vehicles

can fit the carrier vehicle.

Assumptions

• Point mass

• Spherical Earth

• Non-rotating earth

• 3DOF EOM

Flight Segment Method Parameters Segment Termination

1 Acceleration / Climb Constant Q-Climb to Altitude Q =1500 lb/ft2 Cruise Altitude

2 Cruise Constant Mach Cruise for Endurance Time = 0s, 166.6s, 333.3s, 500s Specified Endurance

3 Maneuver Steady Level turn to base n = 2g Heading toward Base

4 Cruise Constant Mach Cruise for Endurance Time = 0s, 166.6s, 333.3s, 500s Specified Endurance

5. Decent 1 Constant Altitude Glide at max L/D N/AEquilibrium Glide

Velocity

6. Decent 2 Equilibrium Glide at max L/D N/A Landing Altitude

Alt

itu

de

Downrange

Cro

ss

ran

ge

Downrange

1 2

3

45

6

1 2

3

4

5

6

Alt

itd

ue

Velocity

102

Figure 5-18 M6 CV10 carriage weight solution space

Figure 5-19 M7 CV10 carriage weight solution space

10000

20000

30000

40000

50000

60000

70000

80000

90000

001 002 003 004 005 006 007 008

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (m^2)


NB-52A/B Limit

22000B-52 Limit

0.080.09

0.110.1

12 min.

16 min.

21 min.

9 min.

X-24C

16000

F-15 Limit

0 lb

1333 lb

2666 lb

4000 lb

10000

20000

30000

40000

50000

60000

70000

80000

90000

100 200 300 400 500 600 700 800

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (ft2)

Mach 6 CV 10 (X-24C) SPLN Solution Space

τ 0.10

τ 0.11

τ 0.09

τ 0.08

10000

20000

30000

40000

50000

60000

70000

80000

90000

001 002 003 004 005 006 007 008

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (m^2)


NB-52A/B Limit

22000B-52 Limit

0.08

0.09

0.110.1

10 min.

17 min.

22 min.

13 min.

X-24C

16000

F-15 Limit

0 lb

1333 lb

2666 lb

4000 lb

10000

20000

30000

40000

50000

60000

70000

80000

90000

100 200 300 400 500 600 700 800

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (ft2)


τ 0.10

τ 0.11

τ 0.09

τ 0.08

103

Figure 5-20 M6 CV10 gross weight landing solution space

Figure 5-21 M7 CV10 gross weight landing solution space

10000

15000

20000

25000

30000

35000

40000

45000

50000

001 002 003 004 005 006

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (m^2)


0.080.09

0.110.1

12 min.

16 min.

21 min.

9 min.

0 lb

1333 lb

2666 lb

4000 lb

10000

15000

20000

25000

30000

35000

40000

45000

50000

150 250 350 450 550 650

Cru

ise

r G

ros

s W

eig

ht

(lb

)

SPLN (ft2)


τ 0.10

τ 0.11

τ 0.09

τ 0.08

10000

15000

20000

25000

30000

35000

40000

45000

50000

001 002 003 004 005 006

Cru

ise

r G

ros

s W

eig

ht

+ B

oo

ste

r W

eig

ht

(lb

)

SPLN (m^2)


0.080.09

0.11

0.1

10 min.

17 min.

22 min.

13 min.

0 lb

1333 lb

2666 lb

4000 lb

10000

15000

20000

25000

30000

35000

40000

45000

50000

150 250 350 450 550 650

Cru

ise

r G

ros

s W

eig

ht

(lb

)

SPLN (ft2)


τ 0.10

τ 0.11

τ 0.09

τ 0.08

104

Figure 5-22 M6 CV10 carriage size solution space

Figure 5-23 M7 CV10 carriage size solution space

B-52 Length Limit

24

B-52 Span Limit

0 lb

1333 lb

2666 lb

4000 lb

30

40

50

60

70

80

90

5 10 15 20 25

Cru

ise

r L

en

gth

+ B

oo

ste

r L

en

gth

(ft

)

Cruiser Span (ft)

Mach 6 CV 10 (X-24C) Span Solution Space

τ 0.10

τ 0.11

τ 0.09

τ 0.08

B-52 Length Limit

24

B-52 Span Limit

0 lb

1333 lb

2666 lb

4000 lb

30

40

50

60

70

80

90

5 10 15 20 25

Cru

ise

r L

en

gth

+ B

oo

ste

r L

en

gth

(ft

)

Cruiser Span (ft)

Mach 7 CV 10 (X-24C) Span Solution Space

τ 0.10

τ 0.11

τ 0.09

τ 0.08

105

5.6 Case Study Conclusion

The case study presented is an excerpt from a comprehensive research effort by

the ASE (Chudoba et al. 2015). Over the course of that study, ASE introduces a unique

product development capability to support decision makers at the acquisition and

technology maturation levels. Three types of solution spaces have been presented

including variations of two mission concepts, thirty-two operational scenarios and two

design speeds, two hardware (two lift sources, two thrust sources, two landing systems,

and two stability and control arrangements) package combinations. This has led to design

and technology relationships being established between 256 sized vehicles. Still, this

composes only a fraction of the mission, operation, and hardware combinations that should

be studied prior to an acquisition decision being made.

5.7 Chapter Summary

This chapter discussed a case study to demonstrate the application of the CMDS

decomposition methodology to a USAF relevant acquisition and conceptual design

problem. The design case study selected was based on the Generic Hypersonic Vehicle

(GHV) project initiated by AFRL to foster hypersonic vehicle design collaboration. The

acquisition problem posed was to determine the best aerospace technology investment to

solve the GHV mission. The conceptual design problem was to determine the technical

feasibility of the chosen technology investment. The CMDS decomposition tools amongst

other tools in AVDDBMS were used to arrive at a prioritized portfolio of candidate vehicle

based on relevant hypersonic demonstrator technologies. Candidate vehicle CV10, which

is based on X-24C lift source and X-51A thrust source technologies, was selected for

investment because of its technology maturity and landing gear system.

The technical feasibility of the CV10 vehicle was evaluated through a parametric

sizing study. The CMDS decomposition tools amongst other tools in AVDDBMS were used

106

to compose a custom CV10 sizing CMDS. The CMDS generated solution spaces for

families of CV10 based vehicles for Mach 6 and Mach 7 design speeds. The solution

spaces were explored to check the feasibility of the vehicles to land on a runway and to be

carried by existing carrier vehicle. Results indicated that none of the vehicles is small

enough to be carried by existing carrier vehicles. It also shows that M6 CV10 vehicles

without payload are feasible if the, now decommission, B52H is the carrier vehicle.

This case study demonstrated the benefit of the CMDS decomposition

methodology to acquisition and conceptual design. First, The CMDS building blocks

enabled the systematic and transparent composition of technology and candidate vehicle

portfolios. The building blocks established by the decomposition allowed the use a

combination of existing technology assessment techniques. Secondly, the decomposition

of existing synthesis systems into the AVDDBMS advanced the capability of the problem-

customize-sizing-tool generator. In summary, the CMDS building blocks are instrumental

in mapping the conceptual design problem to customize the sizing CMDS for the problem

at hand.

107

Chapter 6

RESEARCH CONCLUSION, CONTRIBUTION AND OUTLOOK

6.1 Conclusion

This research endeavor has been initiated in an effort to contribute to the fields of

aerospace technology acquisition and aerospace conceptual design. It is motivated by the

need to mitigate acquisition schedule and cost overruns caused by program management,

politics, supply chain, technical complexity and talent shortage problems. The mitigation

approach chosen has been to increase collaboration and transparency between

technology acquisition assessments and vehicle conceptual design synthesis. A dichotomy

exists between the two fields because state-of-the-art technology assessments utilize

system engineering methodologies that require conceptual designer to impute during the

stakeholder requirements definition phase. However, existing conceptual design synthesis

systems only accept requirements and do not offer methodologies to define them.

Secondly, Acquisition studies require assessment of diverse technology options in order to

arrive at design requirements. However, most synthesis systems have a narrow range of

applicability with reference to flight vehicle configuration, and other perturbations that

should be considered thus be analyzed. This results in the acquisition of technologies that

are well vetted for investment risks but insufficiently evaluated for technical feasibility.

The solution identified to solve the research problem is the development of a

common framework for technology acquisition assessment and conceptual design

synthesis. Due to the enormity of the problem, this framework has been developed in

conjunction with two other PhD researchers. The original contribution to this effort as

documented in the present report has been in the definition of the framework building

blocks. A literature survey has identified the concept of ‘complex multidisciplinary systems’

as the starting point to enable a general building block approach towards problem solving.

108

After this identification, a methodology has been developed for decomposing this block into

constituent parts for both acquisition and conceptual design problems. The product

decomposition blocks, analysis process decomposition block and disciplinary method

blocks are the main parts of the CMDS decomposition scheme presented in this research.

The biggest benefit of the formulation presented is that it enables both, aerospace

technology acquisition portfolio management and conceptual design parametric sizing tool

customization.

AVDDBMS is the software implementation of the unified acquisition and design

platforms. It is a database management software programmed in the MS Access and

MATLAB environment using VBA, SQL and MATLAB script languages. CMDS

decomposition is executed using the decomposition forms available in AVDDBMS. The forms

include product input, process input and disciplinary method input forms. Note that the

CMDS have a broad range of embodiments ranging from reference texts to parametric

sizing tools. As more CMDS are decomposed, the understanding of existing problems and

capability to address new challenges increase.

Finally, a case study has demonstrated the application of the CMDS

decomposition methodology to a relevant acquisition and conceptual design problem. Two

major benefits have been highlighted. First, the CMDS building blocks enabled the

systematic and transparent composition of technology and candidate vehicle portfolios.

The building blocks established by the decomposition allowed the use a combination of

existing technology assessment techniques. Secondly, the decomposition of existing

synthesis systems into the AVDDBMS advanced the ability to customize sizing tool

generation capabilities. The CMDS building blocks are instrumental in mapping the

conceptual design problem towards the customization of a problem-specific sizing tools.

109

6.2 Contributions and Benefits of CMDS Decomposition

The original contributions of this research are as follows:

Identification of complex multidisciplinary systems as common building block for

acquisition and conceptual design problems.

Development of a decomposition scheme reducing CMDS into constituent parts.

Design of the decomposition input forms recording CMDS in the AVDDBMS

framework.

Demonstration of the applicability of CMDS decomposition to a relevant acquisition

and conceptual design problem with USAF endorsement.

6.3 Future Work

The AVDDBMS framework is suitable for real world problems as is. However, there

are some areas can be improved for greater system capability. First, the decomposition of

CMDS is, so far, a manual process. It requires advanced understanding and effort to

decompose new CMDS or poorly described ones. The addition of an Artificial Intelligence

(AI) system to speed up the decomposition process will help mitigate this issue. Secondly,

only two levels of abstraction have been implemented in the decomposition concept in

order to reduce complexity. That is, only a single subsystem decomposition level exists in

addition to the system level. Therefore, if the product is an aircraft, it can only be

decomposed into the hardware level (e.g. scramjet, wing body, landing gear, etc.).

Although subsequent sublevels are captured as attributes, this conception is insufficient

for more complex problems. For example, consider the decomposition of multistage space

launcher. In this conception, the product is the complete vehicle and the subsystems are

the individual vehicle stages. The specification of an actual vehicle as a subsystem instead

110

of the vehicle hardware will not work with the methods in the database as they exist. Further

research is required to determine the best way to handle staged vehicles in the

decomposition methodology that has been developed.

111

METHODS LIBRARY SOURCE CODE

112

A.1 Aerodynamics

AERO_MD0005

%%%%%%%%% Pre-Allocate Outputs %%%%%%%%%

ALDMAX=zeros(size(AMACH));

ALIND=zeros(size(AMACH));

CD0=zeros(size(AMACH));

CLA=zeros(size(AMACH));

CL=zeros(size(AMACH));

CD=zeros(size(AMACH));

%%%%%%%%% Analysis %%%%%%%%%

SWET = AKW*SPLN;

ALIND(AMACH <= 0.8)=0.45; %from fig4-19

ALDMAX(AMACH <= 0.8)= sqrt(pi.*(ECDF/4).*(BPLN./SWET)); %eq on fig 4-13, ECDF

from the plot = 280

CD0(AMACH <= 0.8)=1./(4.*ALIND(AMACH <= 0.8).*ALDMAX(AMACH <= 0.8).^2);

CLA(AMACH <= 0.8)=0.024; %from fig 4-19

CL = CLA.*AOA;

CD = CD0 + ALIND.*CL.^2;

ALD = CL./CD;

AERO_MD0006


ALDMAX = repmat(NaN,size(AMACH));

ALIND = repmat(NaN,size(AMACH));

CD0 = repmat(NaN,size(AMACH));

CLA = repmat(NaN,size(AMACH));

113

CL = repmat(NaN,size(AMACH));

CD = repmat(NaN,size(AMACH));

%%%%%% Regression Data %%%%%%%%%

AMACH_MAP=[1.5, 2.0, 6.0, 12.0];

TAU_MAP=[0.01118,0.041569219,0.051822958,0.064,0.076367532,0.088772738,0.10248638

4,0.117575508, ...

0.132574507,0.147369057,0.164316767,0.181019336,0.198252364,0.216,0.234247732, ...

0.252982213,0.272191109,0.29086856];

ALDMAX_MAP=[8.83,6.85,6.39,5.99,5.64,5.29,4.98,4.68,4.38,4.11,3.85,3.59,3.

34,3.10,2.86,2.61,2.37,2.15;

8.83,6.85,6.39,5.99,5.64,5.29,4.98,4.68,4.38,4.11,3.85,3.59,3.34,3.10,2.86,2.61,2.37,2.15;

8.50,6.32,5.90,5.53,5.19,4.87,4.59,4.31,4.07,3.80,3.56,3.35,3.12,2.89,2.68,2.46,2.26,2.06;

5.67,4.68,4.39,4.14,3.90,3.68,3.49,3.30,3.11,2.93,2.78,2.63,2.48,2.33,2.19,2.05,1.91,1.79];

% %%%%%%%%% Subsonic Analysis %%%%%%%%%

SWET = AKW*SPLN;

ALDMAXS = sqrt(pi.*(ECDF/4).*(BPLN./SWET));

ALINDS = 0.45;

114

CD0S = 1./(4.*ALINDS.*ALDMAXS.^2);

%%%%%%%%% Transonic Analysis %%%%%%%%%

SF=SPLN*SFSPLN;

if (SF/(AL^2) < 0.015)

DCDT_MAX=(1.3862*(SF/AL^2)+0.067)*SFSPLN*CDTW_COR;

else

DCDT_MAX=(0.9536*(SF/AL^2)^3-

1.916*(SF/AL^2)^2+1.3651*(SF/AL^2)+0.1119)*SFSPLN*CDTW_COR;

end

%%%%%%%%% Left side of M1.2 Analysis %%%%%%%%% Same for WB

and AB

CD0(AMACH >= 0.80 & AMACH < 1.2) = CD0S + DCDT_MAX./0.4.*(AMACH(AMACH

>= 0.80 & AMACH < 1.2)-0.8); % linear Drag rise Y = Y0 + m(x-x0)

ALDMAX(AMACH >= 0.80 & AMACH < 1.2)=0.5.*sqrt(1./(ALINDS.*CD0(AMACH >=

0.80 & AMACH < 1.2))); % (L/D)_max = sqrt(cd0/K') Approximation

%%%%%%%%% Right side of M1.2 Analysis %%%%%%%%% Same for AB and WB

CD_2M=1/(4*interp2(TAU_MAP,AMACH_MAP,ALDMAX_MAP,TAU,2.0,'spline').^2*ALINDS);

CD_12M=CD0S+DCDT_MAX;

115

CD0(AMACH >= 1.2 & AMACH <= 2.0)=(CD_2M-CD_12M)./0.8.*(AMACH(AMACH >=

1.2 & AMACH <= 2.0)-1.2)+CD_12M; % linear Drag function Y = Y0 + m(x-x0) from M1.2 to M2

ALDMAX(AMACH >= 1.2 & AMACH <= 2.0)=0.5.*sqrt(1./(CD0(AMACH >= 1.2 &

AMACH <= 2.0)*ALINDS));

%%%%%%%%% Analysis %%%%%%%%%

% CLA(AMACH >= 0.80 & AMACH < 1.5)=(0.022-0.02)./(1.5-0.8).*(AMACH(AMACH

>= 0.80 & AMACH < 1.5)-0.8)+0.020; %Gary (WB)

CLA(AMACH >= 0.80 & AMACH < 1.2)= 0.0052.*AMACH(AMACH >= 0.80 & AMACH

< 1.2) + 0.0202; %Linear relation from excel file

%%%%%%%%% Analysis %%%%%%%%%

%CLA(AMACH >= 1.5 & AMACH <= 2.0)=0.03./AMACH(AMACH >= 1.5 &

AMACH <= 2.0).^0.75+0.00025; %Gary (WB)

CLA(AMACH >= 1.2 & AMACH <= 2.0)=0.0098.*AMACH(AMACH >= 1.2 &

AMACH <= 2.0) + 0.0379; %Linear relation from excel file

%ALIND(AMACH >= 0.8 & AMACH <= 2.0) = 0.47; %Approx Value

ALIND(AMACH >= 0.8 & AMACH <= 2.0)= 0.0651.*(AMACH(AMACH >= 0.8 &

AMACH <= 2.0).^2) - 0.0758.*AMACH(AMACH >= 0.8 & AMACH <= 2.0) + 0.4083; %Polynomial

expression from excel file

CL = CLA.*AOA;


ALD = CL./CD;

AERO_MD0007


116

ALDMAX = repmat(NaN,size(AMACH));

ALIND = repmat(NaN,size(AMACH));

CD0 = repmat(NaN,size(AMACH));

CLA = repmat(NaN,size(AMACH));

%%%%%% Regression Data %%%%%%%%%

AMACH_MAP=[1.5, 2.0, 6.0, 12.0];

TAU_MAP=[0.01118,0.041569219,0.051822958,0.064,0.076367532,0.088772738,0.10248638

4,0.117575508, ...

0.132574507,0.147369057,0.164316767,0.181019336,0.198252364,0.216,0.234247732, ...

0.252982213,0.272191109,0.29086856];

ALDMAX_MAP=[8.83,6.85,6.39,5.99,5.64,5.29,4.98,4.68,4.38,4.11,3.85,3.59,3.

34,3.10,2.86,2.61,2.37,2.15;

8.83,6.85,6.39,5.99,5.64,5.29,4.98,4.68,4.38,4.11,3.85,3.59,3.34,3.10,2.86,2.61,2.37,2.15;

8.50,6.32,5.90,5.53,5.19,4.87,4.59,4.31,4.07,3.80,3.56,3.35,3.12,2.89,2.68,2.46,2.26,2.06;

5.67,4.68,4.39,4.14,3.90,3.68,3.49,3.30,3.11,2.93,2.78,2.63,2.48,2.33,2.19,2.05,1.91,1.79];

%%%%%%%%% Subsonic Analysis %%%%%%%%%

SWET = AKW*SPLN;

ALDMAXS = sqrt(pi.*(ECDF/4).*(BPLN./SWET));

117

ALINDS = 0.45;

CD0S = 1./(4.*ALINDS.*ALDMAXS.^2);

%%%%%%%%% Transonic Analysis %%%%%%%%%

SF=SPLN*SFSPLN;

%DBCD0=interp1(C_SA,DBCD0_MAP,CS_SPAT,'spline');

%ALIND(AMACH >= 2.0)=(2.5-ALINDS)./(12.0-2.0).*(AMACH(AMACH >= 2.0)-

2.0)+ALINDS;

ALIND(AMACH >= 2.0 & AMACH <= 4.0)= 0.081.*(AMACH(AMACH >= 2.0 &

AMACH <= 4.0).^2) - 0.1515.*AMACH(AMACH >= 2.0 & AMACH <= 4.0) + 0.5199; % ?? % add

+ALINDS;

ALIND(AMACH >= 4.0)= 0.1685.*AMACH(AMACH >= 4.0) + 0.5255;

ALDMAX(AMACH >=

2.0)=interp2(TAU_MAP,AMACH_MAP,ALDMAX_MAP,TAU,AMACH(AMACH >= 2.0),'spline');

%same for WB and AB

CD0(AMACH >= 2.0 & AMACH <= 4.0)=1./(4.*ALDMAX(AMACH >= 2.0 &

AMACH <= 4.0).^2.*ALIND(AMACH >= 2.0 & AMACH <= 4.0)); % ?? %Does ALDMAX also go

from 2 to 4 even if it doesnt change from 2 to 12

CD0(AMACH >= 4.0)=1./(4.*ALDMAX(AMACH >= 4.0).^2.*ALIND(AMACH >=

4.0)); % For AB add : + DBCD0./sqrt(AMACH(AMACH >= 2.0).^2-1);

CLA(AMACH >= 2.0)=0.0001.*(AMACH(AMACH >= 2.0).^2) -

0.0029.*AMACH(AMACH >= 2.0) + 0.0243; %from excel file

118

CL = CLA.*AOA;


ALD = CL/CD;

A.2 Propulsion

PROP_MD0008

%% Pre-Allocate HBE_LN

HBE_LN = repmat(NaN,size(THRL_VAR));

%% Pre-Allocate Outputs

AISP = repmat(NaN,size(THRL_VAR)); FT_AVAIL = repmat(NaN,size(THRL_VAR));

OF = repmat(NaN,size(THRL_VAR));

DUCT_PRESSURE = repmat(NaN,size(THRL_VAR)); F_MDOT = repmat(NaN,size(THRL_VAR));

%% Set Up Input Interpolation Arrays

PHI_FUEL = THRL_VAR.*PHI_FUEL_REF;

% Call SCRAM_PERF_LUT

[AISP(AMACH >= 4 & AMACH <= 8.0), F_MDOT(AMACH >= 4 & AMACH <= 8.0), DUCT_PRESSURE(AMACH >= 4 &

AMACH <= 8.0)] = SCRAM_PERF_LUT(ALT(AMACH >= 4 & AMACH <= 8.0), HBE_LN(AMACH >= 4 & AMACH <= 8.0), PHI_FUEL(AMACH >= 4 & AMACH <= 8.0), V(AMACH >= 4 & AMACH <= 8.0));

% Spillage/Additive Effect of Capture Area Ratio f(AMACH)

[A0_A1_SPILLAGE] = A0_A1_Billig(DMACH, AMACH);

% Contraction Area Ratio as a Function of AOA A1_A2_GEO = CR_GEO./cosd(AOA);

% Boundary Layer Displacement Thickness Effect of Capture Area Ratio A2_A3_BLDISPLACEMENT = A_A0_BLDISPLACEMENT;

% Effective Capture Area CR_EFF = A0_A1_SPILLAGE.*A1_A2_GEO.*A2_A3_BLDISPLACEMENT;

% Available Thrust FT_AVAIL = F_MDOT.*RHO.*V.*(CR_EFF).*ACAP;

%% SubFunction function [AISP, F_MDOT, DUCT_PRESSURE] = SCRAM_PERF_LUT(ALT, HBE_LN, PHI_FUEL, V)

%% Load Look Up Table Array Data

if exist('PROP_MD0008') == 0 load C:\AVDS\AVD_ABE\Utilities\Method_Data\PROP_MD0008.mat;

end

%% Look-Up Table Interpolation Arrays

PROP_ALT_MAP = PROP_MD0008.DISCPROC.PROP.OUTPUT.PROP_ALT_MAP;

PROP_HBE_LN_MAP = PROP_MD0008.DISCPROC.PROP.OUTPUT.PROP_HBE_LN_MAP; PROP_PHI_FUEL_MAP = PROP_MD0008.DISCPROC.PROP.OUTPUT.PROP_PHI_FUEL_MAP;

PROP_V_MAP = PROP_MD0008.DISCPROC.PROP.OUTPUT.PROP_V_MAP;

% NDGRID

[PROP_ALT_MAP, PROP_HBE_LN_MAP, PROP_PHI_FUEL_MAP, PROP_V_MAP] = ndgrid(PROP_ALT_MAP,...

119

PROP_HBE_LN_MAP,...

PROP_PHI_FUEL_MAP,...

PROP_V_MAP);

%% Interpolate For AISP, Specific Thrust, DUCT_PRESSURE AISP = interpn(PROP_ALT_MAP, PROP_HBE_LN_MAP, PROP_PHI_FUEL_MAP, PROP_V_MAP, ...

PROP_MD0008.HW.Scramjet_01.PROP.OUTPUT.AISP, ...

ALT, HBE_LN, PHI_FUEL, V,'spline');

F_MDOT = interpn(PROP_ALT_MAP, PROP_HBE_LN_MAP, PROP_PHI_FUEL_MAP, PROP_V_MAP, ...

PROP_MD0008.HW.Scramjet_01.PROP.OUTPUT.F_MDOT, ...

ALT, HBE_LN, PHI_FUEL, V,'spline');

DUCT_PRESSURE = interpn(PROP_ALT_MAP, PROP_HBE_LN_MAP, PROP_PHI_FUEL_MAP, PROP_V_MAP,

...

PROP_MD0008.HW.Scramjet_01.PROP.OUTPUT.DUCT_PRESSURE, ...

ALT, HBE_LN, PHI_FUEL,

V,'spline'); end

function [A0_A1] = A0_A1_Billig(DMACH, AMACH) DATA_S = ...

[5.1270356 0.3717109 5.809769 0.39194015

6.67132 0.42053854

7.5028367 0.44673762 8.423623 0.4765186

10.294952 0.53727454

12.01722 0.59085536 13.442756 0.63611245

14.512325 0.67186326

16.353342 0.7290146 18.106201 0.7874053

19.561771 0.8350617

21.135534 0.883878 22.175346 0.91843486

23.155369 0.94939846

24.135668 0.9815674 25.04978 0.9824202];

AMACH_S = DATA_S(:,1);

A0_A1_S = DATA_S(:,2);

A0_A1_REF = interp1(AMACH_S,A0_A1_S,DMACH,'pchip');

A0_A1_RAW = interp1(AMACH_S,A0_A1_S,AMACH,'pchip');

A0_A1 = A0_A1_RAW./A0_A1_REF;

end

A.3 Performance Matching

PM_MD0003

PM_MD0008

PM_MD0009

120

PM_MD0010

% % PREALLOCATE VECTORS AISP_EFF_V = zeros(TRAJ_NSTEP,1);

AISP_V = zeros(TRAJ_NSTEP,1);

AISP_V_HW = zeros(TRAJ_NSTEP,length(VEHICLE_HW)); ALD_V = zeros(TRAJ_NSTEP,1);

AMACH_V = zeros(TRAJ_NSTEP,1);

AN_V = zeros(TRAJ_NSTEP,1); AOA_V = zeros(TRAJ_NSTEP,1);

CD_V = zeros(TRAJ_NSTEP,1);

CD_V_HW = zeros(TRAJ_NSTEP,length(VEHICLE_HW)); CL_V = zeros(TRAJ_NSTEP,1);

CL_V_HW = zeros(TRAJ_NSTEP,length(VEHICLE_HW));

D_V = zeros(TRAJ_NSTEP,1); DGAM_V = zeros(TRAJ_NSTEP,1);

DPSI_V = zeros(TRAJ_NSTEP,1);

DR_V = zeros(TRAJ_NSTEP,1); DT_V = zeros(TRAJ_NSTEP,1);

DUCT_PRESSURE_V = zeros(TRAJ_NSTEP,1);

DW_V = zeros(TRAJ_NSTEP,1); DWF_V = zeros(TRAJ_NSTEP,1);

DWO_V = zeros(TRAJ_NSTEP,1);

DX_V = zeros(TRAJ_NSTEP,1); DY_V = zeros(TRAJ_NSTEP,1);

EDOT_V = zeros(TRAJ_NSTEP,1); EI_V = zeros(TRAJ_NSTEP,1);

FT_AVAIL_MAX_V = zeros(TRAJ_NSTEP,1);

FT_V = zeros(TRAJ_NSTEP,1); %FT_V_HW = zeros(TRAJ_NSTEP,length(VEHICLE_HW));

G_V = zeros(TRAJ_NSTEP,1);

GAMDOT_V = zeros(TRAJ_NSTEP,1); L_V = zeros(TRAJ_NSTEP,1);

OF_V = zeros(TRAJ_NSTEP,1);

OF_V_HW = zeros(TRAJ_NSTEP,length(VEHICLE_HW));

PSIDOT_V = zeros(TRAJ_NSTEP,1);

QBAR_V = zeros(TRAJ_NSTEP,1);

SELECTED_V_FUNCMODE = cell(TRAJ_NSTEP,length(VEHICLE_FUNCTION)); SIGMA_V = zeros(TRAJ_NSTEP,1);

W_V = zeros(TRAJ_NSTEP,1);

%INITIAL POINTS FROM TRAJECTORY

I = max(I_V);

% CALCULTE CHANGE IN AMACH PER STEP

V_START = V_V(I);

ALT_START = ALT_V(I); X_START = X_V(I);

Y_START = Y_V(I);

PSI_START = PSI_V(I)*DTR;

% ASSIGN CONTROL VARIABLES TO traj

AN_LIM = TRAJ_AN_MAX;

G = G0./(1 + ALT_START./RE).^2;

RTURN = V_START^2/sqrt(AN_LIM^2*G0^2-(G-V_START^2/(RE+ALT_START))^2); SIGMA = acos(1/(AN_LIM*G0)*(G-V_START^2/(RE+ALT_START)));

L0 = sqrt(X_START^2+Y_START^2);

THT0 = atan2(Y_START,X_START); if THT0 < 0

THT0 = 2*pi+THT0;

end THT3 = THT0+pi-(pi/2+PSI_START);

L1 = sqrt(L0^2+RTURN^2-2*L0*RTURN*cos(THT3));

THT1 = asin(RTURN/L1*sin(THT3));

121

THT2 = asin(RTURN/L1);

PSI_FINAL = THT0+THT1+THT2+pi;

% CALCULTE CHANGE IN ALT PER STEP

DPSI = (PSI_FINAL - PSI_START)/(TRAJ_NSTEP+1); PSI_V(I) = PSI_V(I)+DPSI/DTR;

% ITERATE FOR EACH ENERGY LEVEL

for CT = 1:TRAJ_NSTEP %ending at E(N+1) in order to store derivatives for E(N) %INPUTS FROM TRAJECTORY

I = max(I_V);

WR = WR_V(I); GAM = GAM_V(I)*DTR;

PSI = PSI_V(I)*DTR;

V = V_V(I);%sqrt((V_V(I+1).^2 + V_V(I).^2)/2); ALT = ALT_V(I);%(ALT_V(I+1) + ALT_V(I))/2;

%ANALYSIS

FLTCOND = fltcon(ALT,0,V,0);

QBAR=FLTCOND.QBAR; AMACH=FLTCOND.AMACH;

W = WS*SPLN/WR;

G = G0./(1 + ALT./RE).^2;

GAMDOT = 0;

L = W./(G0*cos(SIGMA)) .* (G - V.^2./(RE + ALT));

CL_REQ = L./(QBAR*SPLN);

AOA_OUT = fzero(@(AOA_IN) AOAFUNC(AOA_IN,ALT,V),0,optimset('Display','off','Diagnostic','off')); AOA = AOA_OUT;

D = QBAR.*CD*SPLN;

ALD = L/D;

AN = 0;

THRL_VAR = 1; FT_AVAIL_MAX = FT_AVAIL;

FT = W*(AN + D/W + G/G0*sin(GAM)); % Thrust requirement for max acceleration

if (FT_AVAIL_MAX < FT & INSUFF_THRUST_CHECK == 'Y' )

disp(' I ALT V GAM W FT_AVAIL_MAX FT D

G./G0.*sin(GAM)') disp([I ALT V GAM/DTR W FT_AVAIL_MAX FT D G./G0.*sin(GAM)])

error('INSUFFICIENT THRUST')

end

THRL_VAR_OUT = fsolve(@(THRL_VAR_IN) THRL_VARFUNC(THRL_VAR_IN, AOA,ALT,V,

FT),1,optimset('Display','off','Diagnostic','off','TolFun',1e-4)); THRL_VAR = THRL_VAR_OUT; % Change in THRL_VAR triggers code to call FT function

FT = FT_AVAIL; AISP = AISP;

OF = OF;

DUCT_PRESSURE = DUCT_PRESSURE; THRL_VAR_HW = zeros(size(FT_AVAIL_HW));

THRL_VAR_HW(FT_AVAIL_HW~=0) = THRL_VAR;

AISP_EFF = (FT - D - W*sin(GAM))/(FT/AISP);

ALT_NEXT = ALT; V_NEXT = V;

E0 = ALT_V(I)*RE/(RE+ ALT_V(I)) + V_V(I)^2/(2*G0);

EI = ALT_NEXT*RE/(RE+ALT_NEXT) + V_NEXT^2/(2*G0);

%% %%%%%%%%%%%%%%%%%

% CALCULATE DELTAS TO GET TO CURRENT POINT EDOT = V*AN;%sqrt((V^2+V_V(I+1)^2)/2)*AN;

122

PSIDOT = AN_LIM*G0/V*sin(SIGMA);

XDOT = V*cos(GAM)*cos(PSI)*RE/(RE+ALT); YDOT = V*cos(GAM)*sin(PSI)*RE/(RE+ALT);

DT = DPSI/PSIDOT;

DW = - (FT/AISP)*DT; DWF = - DW/(1+OF);

DWO = OF*DWF;

WRNEXT = 1/(1/WR + DW/(WS*SPLN)); DX = XDOT*DT;

DY = YDOT*DT;

DR = sqrt(DX^2+DY^2); DGAM = GAMDOT*DT;

% ASSIGN VALUES AT CURRENT POINT I_V(I+1,1) = I+1;

ALT_V(I+1,1) = ALT;

FF_V(I+1,1) = FF_V(I) + DWF/(WS*SPLN);

GAM_V(I+1,1) = (GAM+DGAM)/DTR;

PSI_V(I+1,1) = (PSI+DPSI)/DTR;

TIME_V(I+1,1) = TIME_V(I)+DT; RANGE_V(I+1,1) = RANGE_V(I)+DR;

V_V(I+1,1) = V;

WR_V(I+1,1) = WRNEXT; X_V(I+1,1) = X_V(I) + DX;

Y_V(I+1,1) = Y_V(I) + DY;

FT_V_HW(I+1,:) = FT_AVAIL_HW; DUCT_PRESSURE_V_HW(I+1,:) = DUCT_PRESSURE_HW;

THRL_VAR_REQ_V_HW(I+1,:) = THRL_VAR_HW; TRAJSEG_V(I+1,1) = METHOD_TRAJSEG;

RANGE = RANGE_V(I+1,1); X_RANGE = X_V(I+1,1);

ENDURANCE = TIME_V(I+1,1);

WR = WR_V(I+1,1); FF = FF_V(I+1,1);

FT_MAX_HW = max(FT_V_HW,[],1);

DUCT_PRESSURE_MAX_HW = max(DUCT_PRESSURE_V_HW,[],1); THRL_VAR_MAX = max(max(THRL_VAR_REQ_V_HW,[],1));

AISP_EFF_V(CT,1) = AISP_EFF; AISP_V(CT,1) = AISP;

AISP_V_HW(CT,:) = AISP_HW;

ALD_V(CT,1) = ALD; AMACH_V(CT,1) = AMACH;

AN_V(CT,1) = AN;

AOA_V(CT,1) = AOA; CD_V(CT,1) = CD;

CD_V_HW(CT,:) = CD_HW;

CL_V(CT,1) = CL; CL_V_HW(CT,:) = CL_HW;

D_V(CT,1) = D;

DGAM_V(CT,1) = DGAM/DTR; DPSI_V(CT,1) = DPSI/DTR;

DR_V(CT,1) = DR;

DT_V(CT,1) = DT; DUCT_PRESSURE_V(CT,1) = DUCT_PRESSURE;

%DUCT_PRESSURE_V_HW(CT,1) = DUCT_PRESSURE_HW;

DW_V(CT,1) = DW; DWF_V(CT,1) = DWF;

DWO_V(CT,1) = DWO;

DX_V(CT,1) = DX; DY_V(CT,1) = DY;

EDOT_V(CT,1) = EDOT;

EI_V(CT,1) = E0; FT_AVAIL_MAX_V(CT,1) = FT_AVAIL_MAX;

123

FT_V(CT,1) = FT;

%FT_V_HW(CT,:) = FT_AVAIL_HW; G_V(CT,1) = G;

GAMDOT_V(CT,1) = GAMDOT;

L_V(CT,1) = L; OF_V(CT,1) = OF;

OF_V_HW(CT,:) = OF_HW;

QBAR_V(CT,1) = QBAR; SELECTED_V_FUNCMODE(CT,:) = SELECTED_FUNCMODE;

SIGMA_V(CT,1) = SIGMA/DTR;

W_V(CT,1) = W; end

PSI_V(I+1,1) = (pi+atan2(Y_V(I+1),X_V(I+1)))/DTR; % Eliminate roundoff errors

%% SubFunction

function Err = AOAFUNC(AOA_IN,ALT,V)

AOA = AOA_IN; % Change in AOA triggers code to call CL function

ALT = ALT;

V = V; Err = CL-CL_REQ;

end

%% SubFunction

function Err = THRL_VARFUNC(THRL_VAR_IN, AOA,ALT,V, FT)

AOA = AOA; ALT = ALT;

V = V; THRL_VAR = THRL_VAR_IN; % Change in THRL_VAR triggers code to call CL function

Err = abs(FT-FT_AVAIL); endend

A.4 Weight & Balance

WB_MD0003

%%%%%% Analysis %%%%%%%%%

if WR < 1 WR

error('WR < 1 vehicle gained weight over trajectory')

end

%FT_MAX_HW = max(FT_V_HW);

WOX_WF = (1-1/WR)/FF - 1; RHO_PPL=(WOX_WF+1)/(WOX_WF/RHO_OX + 1/RHO_FUEL);

WENG_ALENG = ((1990-1210)/(100-10)*(DUCT_PRESSURE_MAX_HW/6894.75729-10) + (1210-1120)/(70-60)*(BENG/0.0254- 70)+1210)*0.45359237*G0/(3.4);

WENG = sum(WENG_ALENG*ALENG_MOD);

AKSTR=(0.317+EBAND)*TAU^0.206;

WSTR_OEW = AKSTR*SPLN^0.138;

% WEIGHT BUDGET CONSTANTS

%WCPRV=FCPRV*CREW;

%WOPER=85.0*ANCREW+FWPPRV*ANPAX; %FROM HOWE %WOPER=FPRV*(CREW*DAYS)^1.18;

WOPER=0;

WPAX = FWPAX*ANPAX;

WCREW = FWCREW*ANCREW;

WFIX = WUN+FWMND*ANCREW;

124

WPAY = WPAX+WCARGO;

WLG = 0.00916*(WS*SPLN*WR/4.44822162)^1.124*4.44822162;

% WEIGHT BUDGET OEW = (1+AMUA)*(WSTR - WLG + WCHUTE + WENG + WSYS); WSYS = WFIX +

FWSYS*OEW; OEW_W = (WFIX+WENG+WLG) / (1/(1+AMUA)-WSTR_OEW-FWSYS);

OWE_W = OEW_W+WPAY+WCREW;

if ((1/(1+AMUA)-WSTR_OEW-FWSYS) < 0.0)

fprintf('AKSTR = %f\n',AKSTR);

fprintf('WR = %f\n',WR); fprintf('1/(1+AMUA) = %f\n',1/(1+AMUA));

fprintf('1/(1+AMUA)-WSTR_OEW-FWSYS = %f\n',(1/(1+AMUA)-WSTR_OEW-FWSYS));

fprintf('FWSYS = %f\n',FWSYS); fprintf('1/(1+AMUA)-WSTR_OEW-FWSYS = %f\n',1/(1+AMUA)-WSTR_OEW-FWSYS);

%WR

%1/(1+AMUA)

%TW0WR_ETW

%TW0WR_ETW_HW

%FT_MAX_HW %AKSTR*SPLN^0.138

%FWSYS

%(1/(1+AMUA)-AKSTR*SPLN^0.138-FWSYS-(TW0WR_ETW)) error('CONVERGENCE FAILURE:');

% 1/(1+AMUA),AKSTR*SPLN^0.138

% (TW0WR_ETW) end

% VOLUME BUDGET OWE

%VTOTAL = TAU*SPLN^1.5;

VFIX = VUN+AKVMND*ANCREW;

VSYS = VFIX+AKVS*VTOTAL;

VPAY = ANPAX*AKVPAX+(WCARGO/RHO_CARGO/G0); VCREW = (AKVCPRV+AKVCREW)*ANCREW;

VVOID = VTOTAL*AKVV;

% from VPPL = OWE_V*(WR-1)/(RHO_PPL*G0) = VTOTAL - VVOID - VSYS - VENG - VPAY - VCREW

OWE_V = (VTOTAL-VSYS- VP -VPAY-VCREW-VVOID)/((WR-1)/(RHO_PPL*G0));

AIP = RHO_PPL/(WR-1);

% WEIGHT AND VOLUME BREAKFORWN

OWE = OWE_W; OEW = OEW_W;

WSTR = AKSTR*SPLN^0.138*OEW_W; AISTR = WSTR/(SPLN*AKW);

TOGW = OWE*WR; WPPL = TOGW*(1-1/WR);

WFUEL = TOGW*FF;

WOX = WOX_WF*WFUEL; WP = WENG;

WSYS = WFIX + FWSYS*OEW;

AMZFW = OWE+WPAY; AMWE = OWE-WOPER-WCREW;

WMARGIN = OEW-(WOPER+WSYS+WSTR+WP);

%VP = TW0WR_KVE*OWE;

VENG = VP;

VPPL = WPPL/RHO_PPL/9.81; VFUEL = WFUEL/RHO_FUEL/9.81;

VOX = WOX/RHO_FUEL/9.81;

;

125

CV10 CMDS

126

B.1 Input File

%<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> % AVD_ABE Input File For AFRL_SFFP_CV10

%<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

function [Variable] = AFRL_SFFP_CV10 (Variable)

% *************************************************************************

% ArchGen: AFRL_SFFP_CV10 Control Variables % *************************************************************************

%Set X-Vector Variable for FZERO solver %********************************** % SPLN_INIT m^2 Planform area

% WS_INIT N/m^2 Wing loading (i.e. TOGW/S)

% X0 Numerical values for X-Vector %**************************************************************************

Variable.SYSPROC.INPUT.SPLN_INIT = 30;

Variable.SYSPROC.INPUT.WS_INIT = 3000; Variable.SYSPROC.INPUT.X0 = [Variable.SYSPROC.INPUT.SPLN_INIT, Variable.SYSPROC.INPUT.WS_INIT];

%Multipoint Variation %**************************************************** % MODE_DESIGN Design mode

% = 0 for Single Point Without Convergence

% = 1 for Multipoint Variation Without Convergence % = 2 for Single Point With Convergence

% = 3 for Multipoint Variation With Convergence % MV_NAMES Variables to be traded

% MV_init Initial value of trade variables

% MV_SS Variable step sizes % MV_NS Number of Steps

% *************************************************************************

Variable.SYSPROC.INPUT.MODE_DESIGN = 2; Variable.SYSPROC.INPUT.MV_NAMES =

{'Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.TAU','Variable.TRAJSEG.ConstantMachEnduranceCruise

_01_PM_MD0008.INPUT.ENDURANCE_CRUISE','Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.WCAR

GO'};

Variable.SYSPROC.INPUT.MV_init = [0.07,0,0];

Variable.SYSPROC.INPUT.MV_SS = [0.02,166.67,5930.7]; Variable.SYSPROC.INPUT.MV_NS = [3,3,3];

% ************************************************************************* % Constants

% *************************************************************************

%Constant %****************************************************************

%G0 m/s^2 Gravitational acceleration at sealevel

%DTR /degrees Conversion from degrees to radians %RE m Radius of the Earth

%**************************************************************************

Variable.SYSPROC.INPUT.G0 = 9.81; Variable.SYSPROC.INPUT.DTR = pi/180;

Variable.SYSPROC.INPUT.RE = 6371e3;

% *************************************************************************

% Look-Up Table Array Variables

% *************************************************************************

%Look-Up Table Input Arrays %**********************************************

%ALT_RANGE m Flight Altitude Range: [Start,End] %ALT_RES m Flight Altitude Resolution

%V_RANGE m/s Flight Velocity Range: [Start,End]

%V_RES m/s Flight Velocity Resolution %AOA_RANGE m Flight Altitude Range: [Start,End]

%AOA_RES m Flight Altitude Resolution

%THRL_VAR_RANGE m Flight Altitude Range: [Start,End]

127

%THRL_VAR_RES m Flight Altitude Resolution

%************************************************************************** Variable.SYSPROC.INPUT.ALT_RANGE = [0.1,26000];

Variable.SYSPROC.INPUT.ALT_RES = 2000;

Variable.SYSPROC.INPUT.V_RANGE = [0.1,2300]; Variable.SYSPROC.INPUT.V_RES = 300;

Variable.SYSPROC.INPUT.AOA_RANGE = [-2,12];

Variable.SYSPROC.INPUT.AOA_RES = 2; Variable.SYSPROC.INPUT.THRL_VAR_RANGE = [0.01,9];

Variable.SYSPROC.INPUT.THRL_VAR_RES = 0.1;

% *************************************************************************

% Geometry Disciplinary & Method Variables % *************************************************************************

%Method: GEO_MD0002 Hardware: TotalVehicle %****************************

%ALBURNER m Length of Burner (X-Dir)

%ALC_AL Ratio of length of compression surface to length of vehicle

%ALISO_HTHROAT Isolator Length to Height Ratio %ALT_CRUISE_DESIGN m Cruise Altitude at Design Point

%DMACH Design Mach number

%F_A_STOIC Stoichiometric Fuel to Air Ratio %HF KJ/kg Absolute sensible enthalpy of fuel entering combustor

%HPR_FUEL KJ/kg Heat of reaction for the fuel

%HTHROAT m Height of Engine Throat %NTECH NTECH SCRAMJET TECHNOLOGY LEVEL: (1 = OPTIMISTIC HYDROGEN; 2 = MODERATE

HYDROGEN; 3 = KEROSENE) %PHI_FUEL_REF Reference Fuel Equivalence Ratio

%T_FUEL_INJECT K Fuel Injection Temperature

%TAU Küchemann’s tau %THETA1_N degrees First nozzle angle

%THETA2_N degrees Second nozzle angle

%TPB_REF K Reference temperature to estimate the absolute static enthalpy %VF_V3 Ratio of fuel injection total velocity to V3 (combustion inlet)

%VFX_V3 Ratio of fuel injection axial velocity to V3 (combustion inlet)

%************************************************************************** Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.ALBURNER = 1.53;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.ALC_AL = 0.6;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.ALISO_HTHROAT = 20; Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.ALT_CRUISE_DESIGN = 26270;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.DMACH = 7;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.F_A_STOIC = 0.06763; Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.HF = 0;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.HPR_FUEL = 50291680;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.HTHROAT = 0.05; Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.NTECH = 2;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.PHI_FUEL_REF = 1;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.T_FUEL_INJECT = 750; Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.TAU = 0.09;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.THETA1_N = 22;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.THETA2_N = 9; Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.TPB_REF = 222;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.VF_V3 = 0.5;

Variable.HW.TotalVehicle.GEO.TotalVehicle_GEO_MD0002.INPUT.VFX_V3 = 0.5;

% *************************************************************************

% Aerodynamics Disciplinary & Method Variables % *************************************************************************

%Method: AERO_MD0005 Hardware: BlendedBody_01 %************************* %ECDF Ratio of square of oswald efeciency factor to skin friction drag coefficient (e^2/CDF). (HYFAC Vol

2pt2 fig 413 use 160, 200, 240, 280 for wing Body). 280 is recommed for very efficient vehicle

%************************************************************************** Variable.HW.BlendedBody_01.AERO.BlendedBody_01_AERO_MD0005.INPUT.ECDF = 280;

128

%Method: AERO_MD0006 Hardware: BlendedBody_01 %************************* %CDTW_COR Transonic drag rise correction factor

%ECDF Ratio of square of oswald efeciency factor to skin friction drag coefficient (e^2/CDF). (HYFAC Vol

2pt2 fig 413 use 160, 200, 240, 280 for wing Body). 280 is recommed for very efficient vehicle %**************************************************************************

Variable.HW.BlendedBody_01.AERO.BlendedBody_01_AERO_MD0006.INPUT.CDTW_COR = 1.0;

Variable.HW.BlendedBody_01.AERO.BlendedBody_01_AERO_MD0006.INPUT.ECDF = 280;

%Method: AERO_MD0007 Hardware: BlendedBody_01 %*************************

%ECDF Ratio of square of oswald efeciency factor to skin friction drag coefficient (e^2/CDF). (HYFAC Vol 2pt2 fig 413 use 160, 200, 240, 280 for wing Body). 280 is recommed for very efficient vehicle

%**************************************************************************

Variable.HW.BlendedBody_01.AERO.BlendedBody_01_AERO_MD0007.INPUT.ECDF = 280;

% *************************************************************************

% Propulsion Disciplinary & Method Variables

% *************************************************************************

%Method: PROP_MD0008 Hardware: Scramjet_01 %**************************** %A_A0_BLDISPLACEMENT Area Ratio of Viscous Captured Flow to Inviscid Captured Flow

%DMACH Design Mach number

%PHI_FUEL_REF Reference Fuel Equivalence Ratio %**************************************************************************

Variable.HW.Scramjet_01.PROP.Scramjet_01_PROP_MD0008.INPUT.A_A0_BLDISPLACEMENT = 0.95;

Variable.HW.Scramjet_01.PROP.Scramjet_01_PROP_MD0008.INPUT.DMACH = 7; Variable.HW.Scramjet_01.PROP.Scramjet_01_PROP_MD0008.INPUT.PHI_FUEL_REF = 1;

% *************************************************************************

% Performance Matching Disciplinary & Method Variables

% *************************************************************************

%Performance Matching Disciplinary Process Input Variables%****************

%TRAJ_ALT_V_START m Start Point For Vector of altitudes %TRAJ_FF_V_START Start Point For Vector of Fuel fractions

%TRAJ_GAM_V_START degrees Start Point For Vector of flight path angles

%TRAJ_PSI_V_START degrees Start Point For Vector of heading angles %TRAJ_RANGE_V_START m Start Point For Vector of total range

%TRAJ_TIME_V_START s Start Point For Vector of trajetory time

%TRAJ_TRAJSEG_V_START Start Point For Vector of current flight segment string %TRAJ_V_V_START m/s Start Point For Vector of vel

%TRAJ_WR_V_START Start Point For Vector of ratios of final mass at each point in the trajectory to init

%TRAJ_X_V_START m Start Point For Vector of position in x-directio %TRAJ_Y_V_START m Start Point For Vector of position in y-directio

%**************************************************************************

Variable.MISSION.INPUT.TRAJ_ALT_V_START = 21891; Variable.MISSION.INPUT.TRAJ_FF_V_START = 0.0;

Variable.MISSION.INPUT.TRAJ_GAM_V_START = 0.0;

Variable.MISSION.INPUT.TRAJ_PSI_V_START = 0; Variable.MISSION.INPUT.TRAJ_RANGE_V_START = 0.0;

Variable.MISSION.INPUT.TRAJ_TIME_V_START = 0.0;

Variable.MISSION.INPUT.TRAJ_TRAJSEG_V_START = {''}; Variable.MISSION.INPUT.TRAJ_V_V_START = 1481.5;

Variable.MISSION.INPUT.TRAJ_WR_V_START = 1;

Variable.MISSION.INPUT.TRAJ_X_V_START = 0; Variable.MISSION.INPUT.TRAJ_Y_V_START = 0;

%Method: PM_MD0009 Trajectory Segment: Booster Launch_01 %*************** %TRAJ_NSTEP Number of steps in current trajectory segment

%TRAJ_WR Ratio of final mass to initial mass for trajectory segment

%************************************************************************** Variable.TRAJSEG.BoosterLaunch_01_PM_MD0009.INPUT.TRAJ_NSTEP = 1;

Variable.TRAJSEG.BoosterLaunch_01_PM_MD0009.INPUT.TRAJ_WR = 1;

%Method: PM_MD0003 Trajectory Segment: Constant Q Climb_01 %*************

129

%INSUFF_THRUST_CHECK Check for inssufficient thrust in PM

%TRAJ_ALT_END Altitude desired at the end of the trajectory segment %TRAJ_AN_MAX g's Maximum acceleration allowed for current trajectory segment

%TRAJ_AN_MIN g's Minimum acceleration allowed for current trajectory segment

%TRAJ_NSTEP Number of steps in current trajectory segment %**************************************************************************

Variable.TRAJSEG.ConstantQClimb_01_PM_MD0003.INPUT.INSUFF_THRUST_CHECK = 'N';

Variable.TRAJSEG.ConstantQClimb_01_PM_MD0003.INPUT.TRAJ_ALT_END = 26270; Variable.TRAJSEG.ConstantQClimb_01_PM_MD0003.INPUT.TRAJ_AN_MAX = 2.0;

Variable.TRAJSEG.ConstantQClimb_01_PM_MD0003.INPUT.TRAJ_AN_MIN = 0.3;

Variable.TRAJSEG.ConstantQClimb_01_PM_MD0003.INPUT.TRAJ_NSTEP = 20;

%Method: PM_MD0008 Trajectory Segment: Constant Mach Endurance Cruise_01 %

%ENDURANCE_CRUISE s Flight time during cruise %INSUFF_THRUST_CHECK Check for inssufficient thrust in PM

%TRAJ_NSTEP Number of steps in current trajectory segment

%**************************************************************************

Variable.TRAJSEG.ConstantMachEnduranceCruise_01_PM_MD0008.INPUT.ENDURANCE_CRUISE = 500;

Variable.TRAJSEG.ConstantMachEnduranceCruise_01_PM_MD0008.INPUT.INSUFF_THRUST_CHECK = 'N';

Variable.TRAJSEG.ConstantMachEnduranceCruise_01_PM_MD0008.INPUT.TRAJ_NSTEP = 20;

%Method: PM_MD0010 Trajectory Segment: Steady Level Turn_01 %************

%INSUFF_THRUST_CHECK Check for inssufficient thrust in PM %TRAJ_AN_MAX g's Maximum acceleration allowed for current trajectory segment


%************************************************************************** Variable.TRAJSEG.SteadyLevelTurn_01_PM_MD0010.INPUT.INSUFF_THRUST_CHECK = 'N';

Variable.TRAJSEG.SteadyLevelTurn_01_PM_MD0010.INPUT.TRAJ_AN_MAX = 2; Variable.TRAJSEG.SteadyLevelTurn_01_PM_MD0010.INPUT.TRAJ_NSTEP = 20;

%Method: PM_MD0008 Trajectory Segment: Constant Mach Endurance Cruise_02 % %ENDURANCE_CRUISE s Flight time during cruise

%INSUFF_THRUST_CHECK Check for inssufficient thrust in PM

%TRAJ_NSTEP Number of steps in current trajectory segment %**************************************************************************

Variable.TRAJSEG.ConstantMachEnduranceCruise_02_PM_MD0008.INPUT.ENDURANCE_CRUISE = 500;

Variable.TRAJSEG.ConstantMachEnduranceCruise_02_PM_MD0008.INPUT.INSUFF_THRUST_CHECK = 'N'; Variable.TRAJSEG.ConstantMachEnduranceCruise_02_PM_MD0008.INPUT.TRAJ_NSTEP = 20;

%Method: PM_MD0001 Trajectory Segment: Gliding Descent_01 %************** %TRAJ_ALT_END Altitude desired at the end of the trajectory segment


%************************************************************************** Variable.TRAJSEG.GlidingDescent_01_PM_MD0001.INPUT.TRAJ_ALT_END = 0;

Variable.TRAJSEG.GlidingDescent_01_PM_MD0001.INPUT.TRAJ_NSTEP = 5;

% *************************************************************************

% Weight and Balance Disciplinary & Method Variables

% *************************************************************************

%Method: WB_MD0003 Hardware: TotalVehicle %*****************************

%AKVCPRV m^3/person Volume of provision for each crew member (Formerly VPCRW) %AKVCREW m^3/person Volume per crew member (formerly AKCRW)

%AKVMND m^3/person Volume of manned fixed systems per crew member (FCREW in hypersonic convergence)

%AKVPAX m^3/person Volume of each passenger space (formerly V_PAX) %AKVS m^3/m^3 Volume of variable systems per total vehicle volume

%AKVV m^3/m^3 Volume of vehicle void space per total vehicle volume

%AMUA Minimum OWE weight margin %ANCREW Number of crew

%ANPAX Number of passengers

%EBAND m^-0.138 Error band around the structural fraction EBAND (+/- 0.049) %FWCREW N/person Weight of each crew member (formerly WCREW)

%FWMND N/person Weight fixed manned systems per crew member (formerly FMND)

%FWPAX N/person Weight of each passenger (formerly WPAX) %FWPPRV N/person Weight of passenger provisions per passenger (formerly FPRV)

130

%FWSYS kg/kg Weight of variable systems per vehicle dry weight (FSYS in hypersonic convergence)

%RHO_CARGO kg/m^3 Density of the cargo %RHO_FUEL kg/m^3 Density of fuel (formerly FUEL_DEN)

%RHO_OX kg/m^3 Density of oxidizer (formerly OX_DEN)

%VUN m^3 Volume of unmanned fixed system %WCARGO N Weight of cargo

%WUN N Weight of unmanned fixed systems (CUN in Hypersonic Convergence)

%************************************************************************** Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.AKVCPRV = 1.5;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.AKVCREW = 1.5;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.AKVMND = 0.0; Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.AKVPAX = 1.7;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.AKVS = 0.03;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.AKVV = 0.15; Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.AMUA = 0.10;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.ANCREW = 0.0;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.ANPAX = 0.0;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.EBAND = 0.0;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.FWCREW = 129.0*9.81;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.FWMND = 0.0*9.81; Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.FWPAX = 100*9.81;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.FWPPRV = 0.0*9.81;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.FWSYS = 0.16; Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.RHO_CARGO = 240;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.RHO_FUEL = 567.65;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.RHO_OX = 1287.0; Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.VUN = 2.5;

Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.WCARGO = 17792; Variable.HW.TotalVehicle.WB.TotalVehicle_WB_MD0003.INPUT.WUN = 300*9.81;

end

131

B.2 Results

TAU CRUISE_TIME

WCARGO SPLN WS TOGW F1 F2 TIME1 TIME2 TIME3 ISTR IP

THRL_VAR_MAX

OWE_W

8.00E-02 5.55E+02 0

1.86E+01

2.61E+03

4.86E+04

1.80E-09

1.86E-07 0

5.55E+02 0

2.14E+02

2.17E+03 1.00E+00

3.86E+04

9.00E-02 5.51E+02 0

1.76E+01

2.83E+03

4.99E+04

3.82E-10

9.86E-08 0

5.51E+02 0

2.25E+02

1.97E+03 1.00E+00

3.87E+04

1.00E-01 5.47E+02 0

1.65E+01

3.04E+03

5.02E+04

4.01E-09

4.70E-07 0

5.47E+02 0

2.37E+02

1.88E+03 1.00E+00

3.86E+04

1.10E-01 5.44E+02 0

1.53E+01

3.22E+03

4.94E+04

0.00E+00

2.91E-11 0

5.44E+02 0

2.48E+02

1.92E+03 1.00E+00

3.81E+04

8.00E-02 7.05E+02 0

1.87E+01

2.61E+03

4.90E+04

8.64E-12

6.26E-10

1.58E+02

3.89E+02

1.58E+02

2.13E+02

2.13E+03 1.00E+00

3.87E+04

9.00E-02 7.05E+02 0

1.78E+01

2.84E+03

5.04E+04

-1.63E-09

-8.18E-08

1.58E+02

3.88E+02

1.58E+02

2.25E+02

1.92E+03 1.00E+00

3.89E+04

1.00E-01 7.04E+02 0

1.68E+01

3.05E+03

5.11E+04

5.21E-09

1.17E-06

1.58E+02

3.87E+02

1.58E+02

2.36E+02

1.81E+03 1.00E+00

3.89E+04

1.10E-01 7.02E+02 0

1.57E+01

3.24E+03

5.09E+04

4.09E-12

6.55E-11

1.58E+02

3.86E+02

1.58E+02

2.47E+02

1.79E+03 1.00E+00

3.86E+04

8.00E-02 9.80E+02 0

1.98E+01

2.64E+03

5.24E+04

5.40E-10

4.15E-08

3.17E+02

3.47E+02

3.17E+02

2.10E+02

1.79E+03 1.00E+00

3.98E+04

9.00E-02 9.80E+02 0

1.89E+01

2.88E+03

5.44E+04

-1.37E-10

-1.22E-08

3.17E+02

3.47E+02

3.17E+02

2.22E+02

1.61E+03 1.00E+00

4.02E+04

1.00E-01 9.80E+02 0

1.80E+01

3.10E+03

5.58E+04

-4.84E-09

-3.83E-07

3.17E+02

3.46E+02

3.17E+02

2.33E+02

1.50E+03 1.00E+00

4.05E+04

1.10E-01 9.79E+02 0

1.72E+01

3.31E+03

5.69E+04

3.55E-11

5.68E-09

3.17E+02

3.46E+02

3.17E+02

2.42E+02

1.41E+03 1.00E+00

4.06E+04

8.00E-02 1.28E+03 0

2.12E+01

2.68E+03

5.67E+04

-2.49E-10

-1.45E-08

4.75E+02

3.30E+02

4.75E+02

2.06E+02

1.49E+03 1.00E+00

4.11E+04

9.00E-02 1.28E+03 0

2.03E+01

2.92E+03

5.93E+04

-3.04E-10

-1.69E-08

4.75E+02

3.30E+02

4.75E+02

2.18E+02

1.35E+03 1.00E+00

4.18E+04

1.00E-01 1.28E+03 0

1.94E+01

3.16E+03

6.13E+04

-1.18E-09

-6.77E-08

4.75E+02

3.30E+02

4.75E+02

2.29E+02

1.26E+03 1.00E+00

4.23E+04

1.10E-01 1.28E+03 0

1.87E+01

3.39E+03

6.33E+04

1.31E-09

7.54E-08

4.75E+02

3.30E+02

4.75E+02

2.39E+02

1.18E+03 1.00E+00

4.27E+04

8.00E-02 5.53E+02

5.93E+03

2.53E+01

2.34E+03

5.92E+04

-1.44E-10

-1.35E-08 0

5.53E+02 0

1.82E+02

2.37E+03 1.00E+00

4.77E+04

132

9.00E-02 5.51E+02

5.93E+03

2.42E+01

2.58E+03

6.24E+04

-1.78E-10

-1.38E-08 0

5.51E+02 0

1.94E+02

2.01E+03 1.00E+00

4.86E+04

1.00E-01 5.48E+02

5.93E+03

2.31E+01

2.82E+03

6.51E+04

1.62E-10

1.08E-08 0

5.48E+02 0

2.06E+02

1.79E+03 1.00E+00

4.95E+04

1.10E-01 5.46E+02

5.93E+03

2.21E+01

3.04E+03

6.70E+04

2.32E-11

1.89E-09 0

5.46E+02 0

2.17E+02

1.68E+03 1.00E+00

5.00E+04

8.00E-02 7.05E+02

5.93E+03

2.55E+01

2.35E+03

6.00E+04

-1.11E-10

-1.02E-08

1.58E+02

3.88E+02

1.58E+02

1.82E+02

2.26E+03 1.00E+00

4.79E+04

9.00E-02 7.04E+02

5.93E+03

2.42E+01

2.58E+03

6.25E+04

6.87E-11

5.44E-09

1.58E+02

3.88E+02

1.58E+02

1.94E+02

2.00E+03 1.00E+00

4.87E+04

1.00E-01 7.04E+02

5.93E+03

2.30E+01

2.81E+03

6.46E+04

-5.05E-09

-3.08E-07

1.58E+02

3.87E+02

1.58E+02

2.06E+02

1.83E+03 1.00E+00

4.93E+04

1.10E-01 7.03E+02

5.93E+03

2.18E+01

3.02E+03

6.59E+04

-2.77E-11

-2.43E-09

1.58E+02

3.87E+02

1.58E+02

2.17E+02

1.75E+03 1.00E+00

4.97E+04

8.00E-02 9.80E+02

5.93E+03

2.70E+01

2.40E+03

6.49E+04

-3.11E-09

3.77E-07

3.17E+02

3.47E+02

3.17E+02

1.79E+02

1.76E+03 1.00E+00

4.91E+04

9.00E-02 9.80E+02

5.93E+03

2.56E+01

2.64E+03

6.76E+04

1.35E-09

8.97E-08

3.17E+02

3.46E+02

3.17E+02

1.91E+02

1.60E+03 1.00E+00

4.99E+04

1.00E-01 9.80E+02

5.93E+03

2.42E+01

2.87E+03

6.96E+04

-3.07E-07

-4.47E-07

3.17E+02

3.46E+02

3.17E+02

2.03E+02

1.51E+03 1.00E+00

5.05E+04

1.10E-01 9.79E+02

5.93E+03

2.30E+01

3.09E+03

7.11E+04

1.93E-10

1.09E-08

3.17E+02

3.46E+02

3.17E+02

2.15E+02

1.45E+03 1.00E+00

5.11E+04

8.00E-02 1.28E+03

5.93E+03

2.89E+01

2.47E+03

7.15E+04

1.43E-10

9.31E-09

4.75E+02

3.30E+02

4.75E+02

1.76E+02

1.39E+03 1.00E+00

5.07E+04

9.00E-02 1.28E+03

5.93E+03

2.73E+01

2.72E+03

7.43E+04

5.41E-11

3.15E-09

4.75E+02

3.30E+02

4.75E+02

1.88E+02

1.29E+03 1.00E+00

5.16E+04

1.00E-01 1.28E+03

5.93E+03

2.58E+01

2.95E+03

7.62E+04

5.32E-11

2.68E-09

4.75E+02

3.30E+02

4.75E+02

2.00E+02

1.23E+03 1.00E+00

5.22E+04

1.10E-01 1.28E+03

5.93E+03

2.45E+01

3.18E+03

7.80E+04

1.21E-10

5.09E-09

4.75E+02

3.30E+02

4.75E+02

2.12E+02

1.19E+03 1.00E+00

5.28E+04

8.00E-02 5.46E+02

1.19E+04

3.21E+01

2.26E+03

7.25E+04

1.05E-10

1.14E-08 0

5.46E+02 0

1.62E+02

2.07E+03 1.00E+00

5.69E+04

9.00E-02 5.44E+02

1.19E+04

3.04E+01

2.49E+03

7.55E+04

-2.02E-10

-1.83E-08 0

5.44E+02 0

1.74E+02

1.83E+03 1.00E+00

5.77E+04

1.00E-01 5.41E+02

1.19E+04

2.88E+01

2.71E+03

7.83E+04

-7.14E-11

-5.49E-09 0

5.41E+02 0

1.85E+02

1.67E+03 1.00E+00

5.84E+04

1.10E-01 5.39E+02

1.19E+04

2.74E+01

2.93E+03

8.02E+04

-1.33E-10

-9.59E-09 0

5.39E+02 0

1.95E+02

1.58E+03 1.00E+00

5.90E+04

8.00E-02 7.03E+02

1.19E+04

3.23E+01

2.27E+03

7.32E+04

-1.82E-12

-1.16E-10

1.58E+02

3.87E+02

1.58E+02

1.62E+02

2.00E+03 1.00E+00

5.70E+04

9.00E-02 7.03E+02

1.19E+04

3.04E+01

2.49E+03

7.55E+04

-2.83E-10

-2.57E-08

1.58E+02

3.86E+02

1.58E+02

1.74E+02

1.83E+03 1.00E+00

5.77E+04

1.00E-01 7.02E+02

1.19E+04

2.87E+01

2.70E+03

7.74E+04

1.01E-10

8.12E-09

1.58E+02

3.86E+02

1.58E+02

1.85E+02

1.72E+03 1.00E+00

5.82E+04

1.10E-01 7.02E+02

1.19E+04

2.72E+01

2.91E+03

7.92E+04

7.32E-11

4.70E-09

1.58E+02

3.85E+02

1.58E+02

1.95E+02

1.64E+03 1.00E+00

5.88E+04

133

8.00E-02 9.79E+02

1.19E+04

3.39E+01

2.34E+03

7.94E+04

-3.23E-10

-2.96E-08

3.17E+02

3.46E+02

3.17E+02

1.61E+02

1.60E+03 1.00E+00

5.86E+04

9.00E-02 9.79E+02

1.19E+04

3.18E+01

2.56E+03

8.16E+04

-2.45E-10

-1.53E-08

3.17E+02

3.46E+02

3.17E+02

1.73E+02

1.50E+03 1.00E+00

5.92E+04

1.00E-01 9.79E+02

1.19E+04

3.00E+01

2.79E+03

8.37E+04

-1.44E-06

-1.09E-06

3.17E+02

3.46E+02

3.17E+02

1.84E+02

1.42E+03 1.00E+00

5.98E+04

1.10E-01 9.79E+02

1.19E+04

2.85E+01

3.00E+03

8.56E+04

5.55E-11

3.40E-09

3.17E+02

3.45E+02

3.17E+02

1.94E+02

1.36E+03 1.00E+00

6.04E+04

8.00E-02 1.28E+03

1.19E+04

3.59E+01

2.43E+03

8.73E+04

-1.13E-07

-1.36E-07

4.75E+02

3.30E+02

4.75E+02

1.61E+02

1.29E+03 1.00E+00

6.07E+04

9.00E-02 1.28E+03

1.19E+04

3.37E+01

2.67E+03

9.00E+04

-9.63E-10

-6.00E-08

4.75E+02

3.30E+02

4.75E+02

1.73E+02

1.22E+03 1.00E+00

6.14E+04

1.00E-01 1.28E+03

1.19E+04

3.19E+01

2.90E+03

9.25E+04

2.33E-10

1.42E-08

4.75E+02

3.29E+02

4.75E+02

1.84E+02

1.16E+03 1.00E+00

6.21E+04

1.10E-01 1.28E+03

1.19E+04

3.03E+01

3.13E+03

9.48E+04

2.18E-11

1.34E-09

4.75E+02

3.29E+02

4.75E+02

1.94E+02

1.11E+03 1.00E+00

6.28E+04

8.00E-02 5.40E+02

1.78E+04

3.79E+01

2.23E+03

8.47E+04

-1.26E-10

-1.66E-08 0

5.40E+02 0

1.51E+02

1.98E+03 1.00E+00

6.59E+04

9.00E-02 5.37E+02

1.78E+04

3.56E+01

2.45E+03

8.73E+04

-7.61E-10

-7.82E-08 0

5.37E+02 0

1.62E+02

1.81E+03 1.00E+00

6.65E+04

1.00E-01 5.35E+02

1.78E+04

3.36E+01

2.67E+03

8.97E+04

0.00E+00

1.12E-09 0

5.35E+02 0

1.72E+02

1.68E+03 1.00E+00

6.71E+04

1.10E-01 5.32E+02

1.78E+04

3.19E+01

2.87E+03

9.16E+04

-1.82E-12

-5.82E-11 0

5.32E+02 0

1.82E+02

1.60E+03 1.00E+00

6.76E+04

8.00E-02 7.02E+02

1.78E+04

3.82E+01

2.25E+03

8.59E+04

-1.12E-10

-1.44E-08

1.58E+02

3.85E+02

1.58E+02

1.51E+02

1.90E+03 1.00E+00

6.62E+04

9.00E-02 7.01E+02

1.78E+04

3.57E+01

2.46E+03

8.79E+04

-9.64E-11

-1.04E-08

1.58E+02

3.84E+02

1.58E+02

1.62E+02

1.77E+03 1.00E+00

6.66E+04

1.00E-01 7.01E+02

1.78E+04

3.37E+01

2.67E+03

9.00E+04

-7.41E-11

-7.17E-09

1.58E+02

3.84E+02

1.58E+02

1.72E+02

1.67E+03 1.00E+00

6.71E+04

1.10E-01 7.00E+02

1.78E+04

3.19E+01

2.88E+03

9.18E+04

-7.55E-11

-7.12E-09

1.58E+02

3.83E+02

1.58E+02

1.82E+02

1.59E+03 1.00E+00

6.76E+04

8.00E-02 9.79E+02

1.78E+04

4.00E+01

2.34E+03

9.34E+04

-1.26E-09

-1.43E-07

3.17E+02

3.45E+02

3.17E+02

1.52E+02

1.53E+03 1.00E+00

6.82E+04

9.00E-02 9.78E+02

1.78E+04

3.74E+01

2.56E+03

9.58E+04

-3.09E-10

-2.90E-08

3.17E+02

3.45E+02

3.17E+02

1.63E+02

1.44E+03 1.00E+00

6.87E+04

1.00E-01 9.78E+02

1.78E+04

3.53E+01

2.78E+03

9.80E+04

8.14E-11

5.97E-09

3.17E+02

3.45E+02

3.17E+02

1.73E+02

1.36E+03 1.00E+00

6.92E+04

1.10E-01 9.78E+02

1.78E+04

3.34E+01

2.99E+03

1.00E+05

-5.55E-11

-3.03E-09

3.17E+02

3.45E+02

3.17E+02

1.83E+02

1.31E+03 1.00E+00

6.98E+04

8.00E-02 1.28E+03

1.78E+04

4.22E+01

2.45E+03

1.03E+05

2.40E-10

2.32E-08

4.75E+02

3.29E+02

4.75E+02

1.53E+02

1.24E+03 1.00E+00

7.09E+04

9.00E-02 1.28E+03

1.78E+04

3.96E+01

2.69E+03

1.06E+05

7.59E-11

6.43E-09

4.75E+02

3.29E+02

4.75E+02

1.64E+02

1.16E+03 1.00E+00

7.16E+04

1.00E-01 1.28E+03

1.78E+04

3.74E+01

2.91E+03

1.09E+05

-3.50E-08

-6.82E-07

4.75E+02

3.29E+02

4.75E+02

1.75E+02

1.12E+03 1.00E+00

7.21E+04

134

1.10E-01 1.28E+03

1.78E+04

3.55E+01

3.14E+03

1.12E+05

-1.36E-11

-9.02E-10

4.75E+02

3.29E+02

4.75E+02

1.85E+02

1.07E+03 1.00E+00

7.29E+04

135

REFERENCES

Sauser, B., Ramirez-Marquez, J., Magnaye R., and Tan W., 2008. "A Systems approach to expanding the technology readiness level within defense acquisition," International Journal of Defense Acquisition Management, vol. 1, pp. 39-58, 2008.

Sauser, B., Ramirez-Marquez, J., Magnaye R., and Tan W., 2008. "System maturity indices for decision support in defense acquisition process”

Hirschel, E. 2005. Basics of aerothermodynamics. Berlin: Springer.

Krishnamoorthy, C. S., & Rajeev, S. 2000. Artificial intelligence and expert systems for engineers. Boca Raton: CRC Press."

9. Merkhofer, L. 2015. "Project Portfolio Management Tools - Which is the Best?," White Paper

Agarwal, M., & Cagan, J. ~1998!. A blend of different tastes: The language of coffeemakers. Environment and Planning B: Planning and Design 25(2), 205–226.

Agarwal, M., Cagan, J., & Constantine, K.G. 1999. Influencing generative design through continuous evaluation: Associating cost with the coffeemaker shape grammar. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13(4), 253–275.

Azizian, Nazanin, Sarkani, Shahram, Mazzuchi. 2009. A Comprehensive Review and Analysis of Maturity Assessment Approaches for Improved Decision Support to Achieve Efficient Defense Acquisition. World Congress on Engineering and Computer Science. 2009. Vol II

Bagera. (2008, June 12). North American Aviation XB-70 Valkyrie. Retrieved September 9, 2016, from http://bagera3005.deviantart.com/art/XB-70-Valkyrie-88484615

Bar-Yam, Yaneer. 1997. Dynamics of Complex Systems. Reading, Mass: Addison-Wesley.

Bilbro, J. W. 2009. "Technology Maturity Assessment," vol. 2009

Boulding, K. E. 1956. General Systems Theory - The Skeleton of Science.

Bradford, John Edward. 2001. "A Technique for Rapid Prediction of Aftbody Nozzle Performance for Hypersonic Launch Vehicle Design.".

Brown, Bradford. 2010. Introduction to Defense Acquisition Management. Defense Acquisition University Press. Fort Belvoir, VA

136

Chudoba, B. and Gonzalez, L. 2015. "Air-Launched Hypersonic Demonstrator Solution Space Screening - Mathematical Optimization in Multidisciplinary Design" USAF AFRL WP-AFB

Chudoba, B., Coleman, G., Oza, A., Omoragbon, A. et al, 2009. “N+3 Aircraft Primer – Development of Advanced Commercial Transport Aircraft Configurations (N+3 and Beyond) Through the Assessment of Past, present, and Future Technologies,” NASA LaRC ASAB Contract Meeting, AVD Laboratory, The University of Texas at Arlington, 24-25 June 2009

Chudoba, B., Coleman, G., Oza, A., Omoragbon, A. et al, 2009. “Thrust Vectoring Benefits from Control Surface Elimination”, NIA Truss-Braced Wing Synergistic Efficiency Technologies Workshop, August 2009

Chudoba, B., Haney, E., Gonzalez, L., Omoragbon, A., Oza, A., 2015. “Strategic Forecasting in Uncertain Environments: Hypersonic Cruise Vehicle Research & Development Case Study,” No. 1211, Vol. 119, The Aeronautical Journal, January 2015

Chudoba, B., Oza, A., Gonzalez, L. Omoragbon, A., Peng, X., Coley, M. and Rana, L., 2014. ”Assessment of the AVX Aircraft Company Patented Coaxial Rotor Dual Ducted Fan Concept,” Technical Report AVD 2014-03-25, Prepared by AVD Services LLC for the Strategy Department of a Leading Helicopter OEM, March 2014

Chudoba, Bernd. 2001. "Stability and Control of Conventional and Unconventional Aircraft Configurations: A Generic Approach." Books on Demand

Coleman, Gary John. 2010. "Aircraft Conceptual Design - an Adaptable Parametric Sizing." PhD, University of Texas at Arlington.

Corbett, B. and Rosen, D. W. 2004. A Configuration Design Based Method for Platform Commonization for Product Families. Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 18, 21-39

Cornford, S. and Sarsfield, L. 2004. "Quantitative Methods for maturing and Infusing Advanced Spacecraft Technology," IEEE Aerospace Conference Proceedings, pp. 663-681, 2004.

Corning, Gerald. 1976. Supersonic and Subsonic, CTOL and VTOL, Airplane Design. College Park, Md.: Corning.

Courtois, P. J. 1985. On time and space decomposition of complex structures. Communications of the ACM2, 590-603.

Cundiff, D. 2003."Manufacturing Readiness Levels (MRL)," Unpublished Whitepaper

137

Czysz, P. 2004. Hypersonic Convergence - Volume 1. Dayton, Ohio: Air Force Research Laboratory.

Czysz, P. and Vandenkerckhove, J. 2001. ''Transatmospheric Launcher Sizing'', Scramjet Propulsion, editors E.T. Curran and S.N.B. Murthy. Progress in Astronautics and Aeronautics, Vol. 189. AIAA, Reston, VA.

DAU. 2013. Defense Acquisition Guidebook. Defense Acquisition University

Davies, C. and Soremekun, G. 2015 "Phoenix Integration and the Skunk Works® A History of Success, A Path to the Future." Phoenix Integration Inc, http://www.phoenix-int.com/resources/webinars/2015/history-of-success.php.

Deloitte. (2009). Can we afford our own future? Why A&D Programs are Late and Over-budget -and what can be done to fix the problem. Deloitte Development LLC

Deloitte. 2015. 2015 Global Aerospace and Defense Sector Financial Performance Study - Growth Slowing Profits Improving. Deloitte Development LLC

DOC. 2015 "Leading Indicators for the U.S. Aerospace Industry". United States of America Department of Commerce

DoD, 2005. "Technology Readiness Assessment (TRA) Deskbook," Department of Defense.

Dubos, G. F., Saleh J. H. and Braun, R. 2007. "Technology readiness level, schedule risk and slippage in spacecraft design: Data analysis and modeling,"

Finck, R. D., D. E. Hoak, and Douglas Aircraft Company. 1978. USAF Stability and Control Datcom. Dayton, Ohio: Flight Control Division, Air Force Dynamics Laboratory, Wright-Patterson Air Force Base.

Finger, S., and Dixon, J. R. 1989a. A Review of Research in Mechanical Engineering Design. Part I: Descriptive, perspective, and computer-based models of design process. Research in Engineering Design 1, 51-67

Finger, S., and Dixon, J. R. 1989b. A Review of Research in Mechanical Engineering Design. Part II: Representations, analysis, and design for life cycle. Research in Engineering Design 1, 121-137

Gero, J.S., Kazakov, V., & Schnier, T. 1997. Genetic engineering and design problems. In Evolutionary Algorithms in Engineering Applications (Dasgupta, D., & Michalewicz, Z., Eds.), pp. 47-68. Berlin: Springer–Verlag."

Gonzalez, L. 2016 "Complex Multidisciplinary System Composition for Aerospace Vehicle Conceptual Design" PhD, University of Texas at Arlington.

138

Gonzalez–Zugasti, J. P., Otto, K.N., & Baker, J. D. 1998. A method for architecting product platforms with an application to interplanetary mission design. Proc. ASME Design Automation Conf., Atlanta, GA, Paper No. DETC980DAC-5608.

Gove, R. 2007. "Development of an integration ontology for systems operational effectiveness,"" M.S. Thesis Stevens Institute of Technology"

Graettinger, C. P., Garcia, S., Siviy, J., Schenk, R. J. and Syckle, P. J. V. 2002. C. P. Graettinger, S. Garcia, J. Siviy, R. J. Schenk and P. J. V. Syckle, "Technology Readiness Levels" Scale to Support Technology Management in the DoDs ATD/STO Enviornments," Carnegie Mellon Software Institute

Haney, E., Gonzalez, L., Omoragbon, A., Oza, A. and Chudoba, B., 2013. “Integrated Engineering Database: A 21st Century Decision Aid,” Proceedings of the 2013 ASEE Gulf-Southwest Annual Conference, The University of Texas at Arlington, Texas, March 21-23, 2013

Haney, Eric. 2016. "Data Engineering in Aerospace Systems Design & Forecasting." Ph.D. Aerospace Engineering, The University of Texas at Arlington.

Haney, Eric. 2016. "Data Engineering in Aerospace Systems Design & Forecasting." Ph.D. Aerospace Engineering, The University of Texas at Arlington.

Heinrich, M., & Jungst, E. 1991. A resource-based paradigm for the configuring of technical systems from modular components. Proc. Seventh IEEE Conf. on AI Applications, pp. 257-264."

Heinze, Wolfgang. 1994. "Ein Beitrag Zur Quantitativen Analyse Der Technischen Und Wirtschaftlichen Auslegungsgrenzen Verschiedener Flugzeugkonzepte Fur Den Transport Grosser Nutzlasten."Inst. fur Flugzeugbau und Leichtbau, Techn. Univ.

Howe, Denis. 2000. Aircraft Conceptual Design Synthesis. London: Professional Engineering Pub.

Huang, Xiao. 2006. "A Prototype Computerized Synthesis Methodology for Generic Space Access Vehicle (SAV) Conceptual Design.".Ph.D. Aerospace Engineering, The University of Oklahoma

Huang, Z., Huai, J., Liu, X. and Zhu J. 2010. Business Process Decomposition Based on Service Relevance Mining

IEEE. 2007. ISO/IEC 26702 IEEE Std 1220-2005 First Edition 2007-07-15 ISO/IEC Standard for Systems Engineering - Application and Management of the Systems Engineering Process Institute of Electrical and Electronics Engineers Incorporated.

Jackson, Scott. 1997. Systems Engineering for Commercial Aircraft. Aldershot, England; Brookfield, Vt., USA: Ashgate.

139

Janiga, M. and Modigliani, P. 2014. “Think Portfolios, Not Programs,” Defense AT&L.

JAYARAM, S., A. MYKLEBUST, and P. GELHAUSEN. 1992. "ACSYNT - A Standards-Based System for Parametric, Computer Aided Conceptual Design of Aircraft." In : American Institute of Aeronautics and Astronautics. doi:doi:10.2514/6.1992-1268.

Jenkinson, Lloyd R., and James F. Marchman. 2003. Aircraft design projects: for engineering students. Oxford, UK: Butterworth-Heinemann.

Jenkinson, Lloyd R., Simpkin, Paul.,Rhodes, Darren,,. 1999. Civil Jet Aircraft Design. Reston, VA: American Insitute of Aeronautics and Astronautics.

Johnson, R. C. and Benson, R. C. 1984. A Basic Two-stage Decomposition Strategy for Design Optimization. ASME Journal of Mechanisms, Transmission, and Automation in Design 106, 380-386

Kline, S. J. 1995. Conceptual Foundations for Multidisciplinary Thinking. Stanford, Calif.: Stanford University Press.

Kockler, F., T. Withers, J. Poodiack, M. Gierman, and DEFENSE SYSTEMS MANAGEMENT COLL FORT BELVOIR VA. 1990. Systems Engineering Management Guide.

Kolodner, J. 1993. Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann.

Kota, S., & Sethuraman, K. 1998. Managing variety in product families through design for commonality. Proc. ASME Design Theory and Methodology Conf., Atlanta, GA. Paper No. DETC980DTM-5651.

Kroo, I. 2006. "Aircraft Design: Synthesis and Analysis."

Kumar, Bharat, Dale De Remer, and Douglas M. Marshall J.D. 2005. An Illustrated Dictionary of Aviation. New York: McGraw-Hill.

Kusaik, A., Wang, J. and He, D. W. 1996. Negotiation in constraint-based design. ASME Transactions: Journal of Mechanical Design 118, 454-460

Kusiak, A., and Cheng, C. H. 1990. A Branch-and-Bound Algorithm for solving the group technology problem. Annals of Operations Research 26, 415-431

Kusiak, A., and Chow, W. S. 1987. Efficient solving of the group technology problem. Journal of Manufacturing Systems 6, 117-124

Kusiak, Andrew. 1999. Engineering design: Products, processes, and systems. San Diego, CA: Academic.

140

Loftin, Laurence K. 1980. Subsonic Aircraft: Evolution and the Matching of Size to Performance. Washington, D.C.; [Springfield, Va.]: National Aeronautics and Space Administration, Scientific and Technical Branch.

Lovell, D. A. 1980. "Some Experiences with Numerical Optimization in Aircraft Specification and Preliminary Design Studies."ICAS, Paper 80-2.4

Mahafza, S. R. 2005. "A Performance-Based Methodology to Assess Department of Defense Technologies," 2005

Mankins, J. C. 2002. "Approaches to Strategic Research and Technology (R&T) Analysis and Road Mapping," Acta Astronautica, vol. 51, pp. 3-21

Mankins, J. C. 2007. "Technology readiness and risk assessment: A new approach,"

Martin, M.V., & Ishii, K. 1997. Design for variety: development of complexity indices and design charts. Proc. ASME Design for Manufacturing Conf., Paper No. DETC970DFM-4359. Sacramento, CA.

Mathews, S. 2010. "Innovation portfolio architecture," Research-Technology Management, vol. 53, no. 6, pp. 30-40.

McCullers, L. A. 1987. Aircraft Configuration Optimization Including Optimized Flight Profiles. Hampton, Virginia: Kentron International, Inc.

McMillan, B. 1964. "Aerospace Management." Aerospace Management.: pp. 62.

Miele, Angelo. 1962. Flight Mechanics. Reading, Mass: Addison-Wesley Pub. Co

MINZNER, R. A., K. S. CHAMPION, H. L. POND, and AIR FORCE CAMBRIDGE RESEARCH LABS HANSCOM AFB MA. 1959. The Ardc Model Atmosphere, 1959.

Mittal, S., & Falkenheiner, B. 1990. Dynamic constraint satisfaction problems. Proc. AAAI-90 Eighth National Conf. on AI, pp. 25-32.

Morgenthaler, G.W. and Fosdick, G.E., 1964. "Selection of Launch Vehicles, Spacecraft, and Missions for Exploration of the Solar System,” Martin Denver Research Report R-64-6, Martin Company, June

MSDN. "Visual Basic: Windows Controls - TreeView Control." Microsoft Developers Network2016, https://msdn.microsoft.com/en-us/library/aa443492(v=vs.60).aspx.

MSFC, NASA (2012) Project Management and Systems Engineering Handbook

NASA and Zell, H. "Earth's Atmoshperic Layers." NASA, last modified July 15, 20152016, http://www.nasa.gov/mission_pages/sunearth/science/atmosphere-layers2.html.

141

NASA. 2007. NASA Systems Engineering Handbook.

Newcomb, P.J., Bras, B.A., & Rosen, D.W. 1998. Implications of modularity on product design for the life cycle. ASME Journal of Mechanical Design 120(3), 483-490.

Nicolai, Leland M. and Carichner, Grant. 2010 "Fundamentals of Aircraft and Airship Design Volume 1." American Institute of Aeronautics and Astronautics.

Nolte, W. I., Kennedy, B. C. and Dziegeil, R. J. 2004. "Technology Readiness Calculator," Whitepaper, 2004.

Norris, G. and Warwick, G., “USAF’s Hypersonics Road Map Sets Long-term View,” Aviation Week & Space Technology, 26 November 2012

OFFICE OF THE UNDER SECRETARY OF DEFENSE. 1997. Joint Modeling and Simulation System (JMASS), Joint Initial Requirements Document (JIRD).

Omoragbon, A., Coleman, G., Gonzalez, L., Watters, B., and Chudoba, B., 2013. “Feasibility Study of a Thrust Vector Control Transport,” Proceedings of the 2013 ASEE Gulf-Southwest Annual Conference, The University of Texas at Arlington, Texas, March 21-23, 2014

Omoragbon, Amen. 2010. An Integration of A Modern Flight Control System Design Technique into A Conceptual Design Stability and Controls Tool, AeroMech. MS Thesis. Aerospace Engineering, University of Texas at Arlington

OSD. 2015. Department of Defense Risk Issue and Opportunity Management Guide for Defense Acquisition Programs. Departmetn of Defense. Washington, DC

Oza, A. R. 2016 "A Portfolio-based Approach to Evaluate Aerospace R&D Problem Formulation Towards Parametric Synthesis Tool Design" PhD, University of Texas at Arlington.

Paris, S.W. and Hargraves, C.R.; 1996. OTIS 3.0 Manual. Boeing Defense & Space Group, Seattle, WA

Perez, RubenE, PeterW Jansen, and JoaquimR R. A. Martins. 2012. "pyOpt: A Python-Based Object-Oriented Framework for Nonlinear Constrained Optimization." Structural and Multidisciplinary Optimization 45 (1): 101-118. doi:10.1007/s00158-011-0666-3.

Petty, M. and Eric Werner Weisel. 2003. "A Composability Lexicon." Orlando Fl, Simulation Interoperability Standards Organization, March 30 - April 4

Petty, M. and Eric Werner Weisel. 2003. "A Composability Lexicon." Orlando Fl, Simulation Interoperability Standards Organization, March 30 - April 4.

142

Phillips, Warren F. 2010. Mechanics of flight. Hoboken, N.J.: Wiley.

Powell, R. W., et al. 2003: Program to Optimize Simulated Trajectories (POST 2), Volume II, User’s Manual, Version 1.1.6.G, November 2003.

Ramirez-Marquez, J. and Sauser, B. 2009. "System Development Planning via System Maturity Optimization," IEEE Transaction on Engineering Management, 2009.

Raymer, Daniel P. 1999. Aircraft Design: A Conceptual Approach. Reston, VA: American Institute of Aeronautics and Astronautics.

Redshaw, Mary C. (2009) Building On a Legacy: Renewed Focus on Systemms Engineering in Defense Acquisition

Rogers, J. L. 1989 "DeMaid: A Design Manager's Aidde for Intelligent Decomposition User's Guide." NASATM-101575, Langley Research Center, Hampton, VA

"Rosenman, M.A., & Gero, J.S. 1999. Evolving designs by generating

useful complex gene structures. In Evolutionary Design by Computers (Bently, P.J., Ed.). San Francisco, CA: Morgan Kaufmann."

Roskam, J. and W. Anematt. 1991. "AAA (Advanced Aircraft Analysis): A User-Friendly Approach to Preliminary Aircraft Design."ICAS,

Roskam, Jan. 2004. Airplane Design. Lawrence (Kansas): DARcorporation.

Ruttle, B., J. Stork, and G. Liston. 2012. Generic Hypersonic Vehicles for Conceptual Design Analyses. Wright-Patterson AFB, OH: AFLR/RQHT.

Ruttle, B., J. Stork, and G. Liston. 2012. Generic Hypersonic Vehicles for Conceptual Design Analyses. Wright-Patterson AFB, OH: AFLR/RQHT.

Ryan, A. 2007. "A Multidisciplinary Approach to Complex Systems Design." PhD, University of Adelaide, School of Mathematical Sciences.

Sauser, B., Ramirez-Marquez, J., Henry, D., DiMarzio, D., and Rosen, J. 2007. "Methods for estimating system readiness levels," School of Systems and Enterprises, Stevens Institute of Technology, 2007.

Sauser, B., Verma, D., Ramirez-Marquez J. and Gove, R. 2006. "From TRL to SRL: The concept of systems readiness levels," Stevens Institute of Technology.

Schaufele, Roger D. 2000. The Elements of Aircraft Preliminary Design.

Sforza, P. M. 2016. Manned Spacecraft Design Principles.

143

Shashank, T. 2014. Managing Complexity of Aerospace Systems. PhD Defense. Purdue University

Shashank, T., Neema, K., and Delaurentis, D. 2014. Framework for Measuring Complexity of Aerospace Systems.

Shashank, T., Neema, K., Kotegawa, T. and Delaurentis, D. 2011. Measuring Complexity of Aerospace Systems.

Shaw, Mary. 1995. "Architectural Issues in Software Reuse: It's Not just the Functionality, it's the Packaging." ACM SIGSOFT Software Engineering Notes 20 (SI): 3-6. doi:10.1145/223427.211783.

SISO. 2004. Base Object Model (BOM) Template Specification Volume I - Interface BOM. SISO-STD-003.1-2003-DRAFTV0.7 ed.

SMC. 2010. Systems Engineering Requirements and Products. Airforce Space Command. El Segundo, CA

Smith, J. 2004. ""An Alternative to Technology Readiness Levels for Non-Developmental Item (NDI) Software,"" Carnegie Mellon Software Institute, 2004."

Sobieszczanski-Sobieski, J. 1982. "A linear Decomposition Method for Large Optimization Problems - Blueprit for Development," NASA TM-83248, Langley Research Center, Hampton VA

Sobieszczanski-Sobieski, J. 1989. "Multidisciplinary Optimization for Engineering Systems: Achievements and Potential," NASA TM-101566, Langley Research Center, Hampton VA

Soininen, T., Tiihonen, J., Mannisto, T., & Sulonen, R. 1998. Towards a general ontology of configuration. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(4), 357-372.

SteinBock, D. (2014) The Challenges for America's Defense Innovation

Stengel, Robert F. 2004. Flight dynamics. Princeton, N.J.: Princeton University Press.

Steward, D. V. 1981. "Systems Analysis and Management: Structure, Strategy and Design." Petrocelli Books. New York

Stinton, Darrol. 1998. The Anatomy of the Airplane. Reston, VA; Oxford, UK: American Institute of Aeronautics and Astronautics; Blackwell Science.

Szabo, C. and Yong Meng Teo. 2007. "On Syntactic Composability and Model Reuse."IEEE, . doi:10.1109/AMS.2007.74.

144

Torenbeek, Egbert. 2013. Advanced Aircraft Design: Conceptual Design, Analysis, and Optimization of Subsonic Civil Airplanes.

United States. 1900. Federal aviation regulations. Part 23. Part 23. Washington, D.C.: U.S. Dept. of Transportation, Federal Aviation Administration.

United States. 1900. Federal aviation regulations. Part 25. Part 25. Washington, D.C.: U.S. Dept. of Transportation, Federal Aviation Administration.

United States., Air Force Systems Command.,. 1965. Space Planners Guide. [Washington, D.C.]: U.S. G.P.O.

United States., Department of Defense. 1974. Engineering Management. Washington, D.C.: Department of Defense, United States of America.

USAF. 2011. SMC Systems Engineering Speciality Engineering Disciplines - Framework and Descriptions. US Air Force Space & Missile System Center

Vinh, Nguyen X. 1981. Optimal Trajectories in Atmospheric Flight. Vol. 2. Amsterdam; New York: Elsevier Scientific Pub. Co.

Vinh, Nguyen X. 1993. Flight mechanics of high-performance aircraft. Cambridge: Cambridge University Press.

Vought, Aero. 1985. Aircraft Synthesis Analysis Program Description Volumes II - IX. Dallas, Texas: LTV Aerospace and Defense, Vought Aero Products Division.

W. I. Nolte, B. C. Kennedy and R. J. Dziegeil, "Technology Readiness Calculator," Whitepaper, 2004.

Weisel, Eric Werner. 2004. "Models, Composability, and Validity." PhD, Old Dominion University.

Wittman, Jr, L. Robert, and Cynthia T. Harrison. 2001. OneSAF: A Product Line Approach to Simulation Development.

Wood, Karl Dawson. 1963. Aerospace Vehicle Design: Vol. 1. Aircraft Design. [Place of publication not identified]: Johnson.

145

BIOGRAPHICAL INFORMATION

Amen Omoragbon is a Nigeria born God fearing follower of Jesus Christ. During

his secondary school days in Nigeria, he fell in love with airplanes. Enticed by their curves

and the physics behind their gravity defying motion, he made a decision learn how to design

these vehicles. He began his colligate career in spring 2004 at the University of North

Texas (UNT) and transferred to the University of Texas at Arlington (UTA) following year.

He earned a Bachelor of Science degree in Aerospace Engineering from UTA in 2008.

During his undergraduate studies, he joined various engineering honors societies, such as

Tau Beta Pi and Pi Tau Sigma, and was elected as the chapter president of the Nation

Society of Black Engineers. In addition to these activities, he was a member of the

Autonomous Vehicle Laboratory at UTA which won multiple awards at the International

AUVSI Unmanned Air Systems competitions.

He continued with his graduate studies at the University of Texas at Arlington in

fall of 2008. In the following summer, he joined the Aerospace Vehicle Design (AVD)

laboratory. He joined the AVD laboratory because he recognized it was a path to realize

his childhood dream of designing airplanes. In 2010, He earned a Master’s of Science

degree in Aerospace Engineering and then embarked on this current research undertaking.

As a member of the lab, he has worked on various aerospace technology and design

projects including assessment of technologies for advancing commercial transports,

evaluation of trust vectoring technologies, performance sizing of electric aircraft

technologies, creation of a high-speed technology investment databases, technical

feasibility assessment of a novel rotorcraft technology and solution space screening of an

air-launched hypersonic demonstrators. He is an author to a Journal article, 5 Conference

Papers, 1 Contract Report and 3 Contract Presentations. Following this Ph.D. research, he

146

plans to pursue a career aerospace technology acquisition and conceptual design

consulting.

Documents

COMPLEX MULTIDISCIPLINARY SYSTEMS DECOMPOSITION FOR